Context tag integration with named entity recognition models

ABSTRACT

Techniques are provided for using context tags in named-entity recognition (NER) models. In one particular aspect, a method is provided that includes receiving an utterance, generating embeddings for words of the utterance, generating a regular expression and gazetteer feature vector for the utterance, generating a context tag distribution feature vector for the utterance, concatenating or interpolating the embeddings with the regular expression and gazetteer feature vector and the context tag distribution feature vector to generate a set of feature vectors, generating an encoded form of the utterance based on the set of feature vectors, generating log-probabilities based on the encoded form of the utterance, and identifying one or more constraints for the utterance.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit and priority of U.S.Provisional Application No. 63/139,569, filed on Jan. 20, 2021, theentire contents of which are incorporated herein by reference for allpurposes.

FIELD

The present disclosure relates generally to chatbot systems, and moreparticularly, to techniques for adding context tags to Named EntityRecognition (NER) models.

BACKGROUND

People around the world use instant messaging or chat platforms in orderto get instant reactions. Organizations often use these instantmessaging or chat platforms to engage with customers (or end users) inlive conversations. However, it can be very costly for organizations toemploy service people to engage in live communication with customers orend users. Chatbots or bots have been developed to simulateconversations with end users, especially over the Internet. End userscan communicate with such bots through messaging apps. An intelligentbot, generally a bot powered by artificial intelligence (AI), cancommunicate intelligently and contextually in live conversations withend users, which allows for a more natural conversation and an improvedconversational experience. Instead of relying on a fixed set of keywordsor commands, intelligent bots may be able to receive utterances of endusers in natural language, understand their intentions, and respondaccordingly.

However, chatbots are difficult to build because they require specificknowledge in certain fields and the application of certain techniquesthat may be solely within the capabilities of specialized developers. Tobuild these chatbots, developers seek to understand the needs of endusers and build machine learning (ML) model(s) tailored to their needs.The task of building ML model(s) typically involves developing andtesting multiple models using unsupervised and/or supervisedlearning-based solutions. In some cases, building an ML model involves atraining phase, an application (i.e., inference) phase, and iterativeloops between the training phase and the application phase. In somecases, precise training data is required to enable the algorithms tounderstand and learn certain patterns or features such that the trainedML model(s) can predict a desired outcome (e.g., inference of an intentfrom an utterance).

BRIEF SUMMARY

Techniques are disclosed for adding context tags to NER models.

In various embodiments, a computer-implemented method comprisesreceiving, at a chatbot system comprising a processor, at least oneutterance comprising one or more words, generating, by atransformer-based model of the chatbot system, a plurality of embeddingsfor the one or more words of the at least one utterance, generating, bya first vectorizer of the chatbot system, at least one regularexpression and gazetteer feature vector for the at least one utterance,generating, by a second vectorizer of the chatbot system, at least onecontext tag distribution feature vector for the at least one utterance,concatenating or interpolating the plurality of embeddings with the atleast one regular expression and gazetteer feature vector and the atleast one context tag distribution feature vector to generate a firstset of feature vectors, generating, by a main sequence model of thechatbot system, an encoded form of the at least one utterance based onthe first set of feature vectors, generating, by a discriminative modelof the chatbot system, a plurality of log-probabilities for candidateentities based on the encoded form of the at least one utterance, andidentifying, using the plurality of log-probabilities, one or moreconstraints for the at least one utterance based on the candidateentities.

In some embodiments, the least one utterance comprises at least one ofone or more queries of the chatbot system, one or more queries input tothe chatbot system by a user, one or more responses provided by the userin response to the one or more queries of the chatbot system, orcombination thereof.

In some embodiments, the transformer-based model of the chatbot systemcomprises of a bidirectional encoder representations from transformersmodel.

In some embodiments, the first vectorizer generates the at least oneregular expression and gazetteer feature vector based on one or moreregular expression patterns and one or more gazetteers.

In some embodiments, wherein the second vectorizer generates the atleast one context tag distribution feature vector based on a context ofat least one of one or more queries of the chatbot system, one or morequeries input to the chatbot system by a user, one or more responsesprovided by the user in response to the one or more queries of thechatbot system, or a combination thereof.

In some embodiments, the main sequence model of the chatbot systemcomprises a combined convolutional neural network/bidirectional longshort-term memory model.

In some embodiments, the discriminative model of the chatbot systemcomprises a conditional random field model.

Some embodiments of the present disclosure include a system includingone or more data processors and a non-transitory computer readablestorage medium containing instructions which, when executed on the oneor more data processors, cause the one or more data processors toperform part or all of one or more methods and/or part or all of one ormore processes disclosed herein.

Some embodiments of the present disclosure include a computer-programproduct tangibly embodied in a non-transitory machine-readable storagemedium, including instructions configured to cause one or more dataprocessors to perform part or all of one or more methods and/or part orall of one or more processes disclosed herein.

The techniques described above and below may be implemented in a numberof ways and in a number of contexts. Several example implementations andcontexts are provided with reference to the following figures, asdescribed below in more detail. However, the following implementationsand contexts are but a few of many.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified block diagram of a distributed environmentincorporating an exemplary embodiment.

FIG. 2 is a simplified block diagram of a computing system implementinga master bot according to certain embodiments.

FIG. 3 is a simplified block diagram of a computing system implementinga skill bot according to certain embodiments.

FIG. 4A is a simplified block diagram of a chatbot training anddeployment system in accordance with various embodiments.

FIG. 4B is a simplified block diagram of a Named Entity Recognition(NER) architecture in accordance with various embodiments.

FIG. 5 illustrates a process flow for taking context into considerationfor entity recognition in accordance with various embodiments.

FIG. 6 depicts a simplified diagram of a distributed system forimplementing various embodiments.

FIG. 7 is a simplified block diagram of one or more components of asystem environment by which services provided by one or more componentsof an embodiment system may be offered as cloud services, in accordancewith various embodiments.

FIG. 8 illustrates an example computer system that may be used toimplement various embodiments.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofcertain embodiments. However, it will be apparent that variousembodiments may be practiced without these specific details. The figuresand description are not intended to be restrictive. The word “exemplary”is used herein to mean “serving as an example, instance, orillustration.” Any embodiment or design described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother embodiments or designs.

Introduction

A digital assistant is an artificially intelligent driven interface thathelps users accomplish a variety of tasks in natural languageconversations. For each digital assistant, a customer may assemble oneor more skills. Skills (also described herein as chatbots, bots, orskill bots) are individual bots that are focused on specific types oftasks, such as tracking inventory, submitting timecards, and creatingexpense reports. When an end user engages with the digital assistant,the digital assistant evaluates the end user input and routes theconversation to and from the appropriate chatbot. The digital assistantcan be made available to end users through a variety of channels such asFACEBOOK® Messenger, SKYPE MOBILE® messenger, or a Short Message Service(SMS). Channels carry the chat back and forth from end users on variousmessaging platforms to the digital assistant and its various bots. Thechannels may also support user agent escalation, event-initiatedconversations, and testing.

Intents allow the chatbot to understand what the user wants the chatbotto do. Intents are the user's intention communicated to the chatbot viauser requests and statements, which are also referred to as utterances(e.g., get account balance, make a purchase, etc.). As used herein, anutterance or a message may refer to a set of words (e.g., one or moresentences) exchanged during a conversation with a chatbot. Intents maybe created by providing a name that illustrates some user action (e.g.,order a pizza) and compiling a set of real-life user statements, orutterances that are commonly associated with triggering the action.Because the chatbot's cognition is derived from these intents, eachintent may be created from a data set that is robust (one to two dozenutterances) and varied, so that the chatbot may interpret ambiguous userinput. A rich set of utterances enables a chatbot to understand what theuser wants when it receives messages like “Forget this order!” or“Cancel delivery!”—messages that mean the same thing but are expresseddifferently. Collectively, the intents, and the utterances that belongto them, make up a training corpus for the chatbot. By training analgorithm with the corpus, a customer turns that algorithm into a modelthat serves as a reference tool for resolving end user input(s) to asingle intent. A customer can improve the acuity of the chatbot'scognition through rounds of intent testing and intent training.

However, building a chatbot that can determine end users' intents basedupon the end users' utterances is a challenging task at least due to thesubtleties and ambiguity of natural languages and the dimensions of theinput/output space (e.g., possible user utterances, number of intents,etc.). An illustrative example of this difficulty arises fromcharacteristics of natural language, such as employing euphemisms,synonyms, or ungrammatical speech to express intent. For example, anutterance may express an intent to order a pizza without explicitlymentioning the words pizza, ordering, or delivery. These characteristicsof natural language give rise to uncertainty and result in chatbotsusing confidence as a parameter for prediction of user intents. As such,chatbots may need to be trained, monitored, debugged, and retrained inorder to improve the performance of the chatbot and user experience withthe chatbot. In conventional spoken language understanding (SLU) andnatural language processing (NLP) systems, training mechanisms areprovided for training and retraining machine-learning algorithms of thedigital assistant or chatbot included therein. Conventionally, thesealgorithms are trained with “manufactured” utterances for any intent.For example, the utterance “Do you do price changes?” may be used totrain a classification algorithm of a chatbot system to classify thistype of utterance as the intent—“Do you offer a price match.” Thetraining of algorithms with manufactured utterances helps initiallytrain the chatbot system for providing services and re-train the chatbotsystem once it is deployed and receives utterances from users.

A user utterance may contain a name entity. In addition to intention,named entities further allow a chatbot to understand the meaning of theuser's utterances. Named entities modify an intent(s). For example, if auser types “show me yesterday's financial news”, the named entities“yesterday” and “financial” assist the chatbot in understanding theuser's request. Entities may be categorized according to what theyrepresent. For example, “yesterday” may be categorized as “dateTime” and“financial” may be categorized as “newsType.” Entities are sometimesreferred to as slots. Named Entity Recognition (NER) is a tool used bychatbot systems to automatically recognize and extract entities. NERtypically involves named entity resolution and named entitydisambiguation. Named entity resolution involves identifying namedentities in a sequence of words and name entity disambiguation involvesidentifying the exact referent of each named entity in the sequence ofwords. For example, with respect to the toponym: “Paris”. People willgenerally assume that the referent of this entity is the city of Parisin France since the city of Paris in France is widely known. However,there are other possible referents for the toponym “Paris” (e.g.,referents may include Paris, Tex., USA; Paris, Ontario, Canada; Paris,Panama; Paris, Togo, etc.). Additionally, referents may include a personnamed Paris or a commercial entity or business enterprise named Paris.Because the referent of a named entity may not always correspond to anobvious or popular referent, identifying the intended referent ischallenging.

To overcome these challenges and others to correctly identify theintended referent for a particular named entity, the approachesdescribed herein consider context of the intended referent. In variousembodiments, a method is provided that includes receiving at least oneutterance, generating embeddings for one or more words of the at leastone utterance, generating at least one regular expression and gazetteerfeature vector for the at least one utterance, generating at least onecontext tag distribution feature vector for the at least one utterance,concatenating or interpolating the embeddings with the at least oneregular expression and gazetteer feature vector and the at least onecontext tag distribution feature vector to generate a first set offeature vectors, generating an encoded form of the at least oneutterance based on the first set of feature vectors, generating aplurality of log-probabilities for candidate entities based on theencoded form of the at least one utterance, and identifying, using theplurality of log-probabilities, one or more constraints for the at leastone utterance based on the candidate entities. Other features andadvantages of the various embodiments are apparent throughout thisdisclosure.

Bot Systems

A bot (also referred to as a skill, chatbot, chatterbot, or talkbot) isa computer program that can perform conversations with end users. Thebot can generally respond to natural-language messages (e.g., questionsor comments) through a messaging application that uses natural-languagemessages. Enterprises may use one or more bots to communicate with endusers through a messaging application. The messaging application mayinclude, for example, over-the-top (OTT) messaging channels (such asFacebook Messenger, Facebook WhatsApp, WeChat, Line, Kik, Telegram,Talk, Skype, Slack, or SMS), virtual private assistants (such as AmazonDot, Echo, or Show, Google Home, Apple HomePod, etc.), mobile and webapp extensions that extend native or hybrid/responsive mobile apps orweb applications with chat capabilities, or voice based input (such asdevices or apps with interfaces that use Siri, Cortana, Google Voice, orother speech input for interaction).

In some examples, the bot may be associated with a Uniform ResourceIdentifier (URI). The URI may identify the bot using a string ofcharacters. The URI may be used as a webhook for one or more messagingapplication systems. The URI may include, for example, a UniformResource Locator (URL) or a Uniform Resource Name (URN). The bot may bedesigned to receive a message (e.g., a hypertext transfer protocol(HTTP) post call message) from a messaging application system. The HTTPpost call message may be directed to the URI from the messagingapplication system. In some examples, the message may be different froma HTTP post call message. For example, the bot may receive a messagefrom a Short Message Service (SMS). While discussion herein refers tocommunications that the bot receives as a message, it should beunderstood that the message may be an HTTP post call message, a SMSmessage, or any other type of communication between two systems.

End users interact with the bot through conversational interactions(sometimes referred to as a conversational user interface (UI)), just asend users interact with other people. In some cases, the conversationalinteractions may include the end user saying “Hello” to the bot and thebot responding with a “Hi” and asking the end user how it can help. Endusers also interact with the bot through other types of interactions,such as transactional interactions (e.g., with a banking bot that is atleast trained to transfer money from one account to another),informational interactions (e.g., with a human resources bot that is atleast trained check the remaining vacation hours the user has), and/orretail interactions (e.g., with a retail bot that is at least trainedfor discussing returning purchased goods or seeking technical support).

In some examples, the bot may intelligently handle end user interactionswithout intervention by an administrator or developer of the bot. Forexample, an end user may send one or more messages to the bot in orderto achieve a desired goal. A message may include certain content, suchas text, emojis, audio, image, video, or other method of conveying amessage. In some examples, the bot may automatically convert contentinto a standardized form and generate a natural language response. Thebot may also automatically prompt the end user for additional inputparameters or request other additional information. In some examples,the bot may also initiate communication with the end user, rather thanpassively responding to end user utterances.

A conversation with a bot may follow a specific conversation flowincluding multiple states. The flow may define what would happen nextbased on an input. In some examples, a state machine that includes userdefined states (e.g., end user intents) and actions to take in thestates or from state to state may be used to implement the bot. Aconversation may take different paths based on the end user input, whichmay impact the decision the bot makes for the flow. For example, at eachstate, based on the end user input or utterances, the bot may determinethe end user's intent in order to determine the appropriate next actionto take. As used herein and in the context of an utterance, the term“intent” refers to an intent of the user who provided the utterance. Forexample, the user may intend to engage the bot in a conversation toorder pizza, where the user's intent would be represented through theutterance “order pizza.” A user intent can be directed to a particulartask that the user wishes the bot to perform on behalf of the user.Therefore, utterances reflecting the user's intent can be phrased asquestions, commands, requests, and the like.

In the context of the configuration of the bot, the term “intent” isalso used herein to refer to configuration information for mapping auser's utterance to a specific task/action or category of task/actionthat the bot can perform. In order to distinguish between the intent ofan utterance (i.e., a user intent) and the intent of the bot, the latteris sometimes referred to herein as a “bot intent.” A bot intent maycomprise a set of one or more utterances associated with the intent. Forinstance, an intent for ordering pizza can have various permutations ofutterances that express a desire to place an order for pizza. Theseassociated utterances can be used to train an intent classifier of thebot to enable the intent classifier to subsequently determine whether aninput utterance from a user matches the order pizza intent. Bot intentsmay be associated with one or more dialog flows for starting aconversation with the user and in a certain state. For example, thefirst message for the order pizza intent could be the question “Whatkind of pizza would you like?” In addition to associated utterances, botintents may further comprise named entities that relate to the intent.For example, the order pizza intent could include variables orparameters used to perform the task of ordering pizza (e.g., topping 1,topping 2, pizza type, pizza size, pizza quantity, and the like). Thevalue of an entity is typically obtained through conversing with theuser.

FIG. 1 is a simplified block diagram of an environment 100 incorporatinga chatbot system according to certain embodiments. Environment 100comprises a digital assistant builder platform (DABP) 102 that enablesusers 104 of DABP 102 to create and deploy digital assistants or chatbotsystems. DABP 102 can be used to create one or more digital assistants(or DAs) or chatbot systems. For example, as shown in FIG. 1, users 104representing a particular enterprise can use DABP 102 to create anddeploy a digital assistant 106 for users of the particular enterprise.For example, DABP 102 can be used by a bank to create one or moredigital assistants for use by the bank's customers. The same DABP 102platform can be used by multiple enterprises to create digitalassistants. As another example, an owner of a restaurant (e.g., a pizzashop) may use DABP 102 to create and deploy a digital assistant thatenables customers of the restaurant to order food (e.g., order pizza).

For purposes of this disclosure, a “digital assistant” is a tool thathelps users of the digital assistant accomplish various tasks throughnatural language conversations. A digital assistant can be implementedusing software only (e.g., the digital assistant is a digital toolimplemented using programs, code, or instructions executable by one ormore processors), using hardware, or using a combination of hardware andsoftware. A digital assistant can be embodied or implemented in variousphysical systems or devices, such as in a computer, a mobile phone, awatch, an appliance, a vehicle, and the like. A digital assistant isalso sometimes referred to as a chatbot system. Accordingly, forpurposes of this disclosure, the terms digital assistant and chatbotsystem are interchangeable.

A digital assistant, such as digital assistant 106 built using DABP 102,can be used to perform various tasks via natural language-basedconversations between the digital assistant and its users 108. As partof a conversation, a user may provide one or more user inputs 110 todigital assistant 106 and get responses 112 back from digital assistant106. A conversation can include one or more of inputs 110 and responses112. Via these conversations, a user can request one or more tasks to beperformed by the digital assistant and, in response, the digitalassistant is configured to perform the user-requested tasks and respondwith appropriate responses to the user.

User inputs 110 are generally in a natural language form and arereferred to as utterances. A user utterance 110 can be in text form,such as when a user types in a sentence, a question, a text fragment, oreven a single word and provides it as input to digital assistant 106. Insome examples, a user utterance 110 can be in audio input or speechform, such as when a user says or speaks something that is provided asinput to digital assistant 106. The utterances are typically in alanguage spoken by the user. For example, the utterances may be inEnglish, or some other language. When an utterance is in speech form,the speech input is converted to text form utterances in that particularlanguage and the text utterances are then processed by digital assistant106. Various speech-to-text processing techniques may be used to converta speech or audio input to a text utterance, which is then processed bydigital assistant 106. In some examples, the speech-to-text conversionmay be done by digital assistant 106 itself.

An utterance, which may be a text utterance or a speech utterance, canbe a fragment, a sentence, multiple sentences, one or more words, one ormore questions, combinations of the aforementioned types, and the like.Digital assistant 106 is configured to apply natural languageunderstanding (NLU) techniques to the utterance to understand themeaning of the user input. As part of the NLU processing for anutterance, digital assistant 106 is configured to perform processing tounderstand the meaning of the utterance, which involves identifying oneor more intents and one or more entities corresponding to the utterance.Upon understanding the meaning of an utterance, digital assistant 106may perform one or more actions or operations responsive to theunderstood meaning or intents. For purposes of this disclosure, it isassumed that the utterances are text utterances that have been provideddirectly by a user of digital assistant 106 or are the results ofconversion of input speech utterances to text form. This however is notintended to be limiting or restrictive in any manner.

For example, a user input may request a pizza to be ordered by providingan utterance such as “I want to order a pizza.” Upon receiving such anutterance, digital assistant 106 is configured to understand the meaningof the utterance and take appropriate actions. The appropriate actionsmay involve, for example, responding to the user with questionsrequesting user input on the type of pizza the user desires to order,the size of the pizza, any toppings for the pizza, and the like. Theresponses provided by digital assistant 106 may also be in naturallanguage form and typically in the same language as the input utterance.As part of generating these responses, digital assistant 106 may performnatural language generation (NLG). For the user ordering a pizza, viathe conversation between the user and digital assistant 106, the digitalassistant may guide the user to provide all the requisite informationfor the pizza order, and then at the end of the conversation cause thepizza to be ordered. Digital assistant 106 may end the conversation byoutputting information to the user indicating that the pizza has beenordered.

At a conceptual level, digital assistant 106 performs various processingin response to an utterance received from a user. In some examples, thisprocessing involves a series or pipeline of processing steps including,for example, understanding the meaning of the input utterance,determining an action to be performed in response to the utterance,where appropriate causing the action to be performed, generating aresponse to be output to the user responsive to the user utterance,outputting the response to the user, and the like. The NLU processingcan include parsing the received input utterance to understand thestructure and meaning of the utterance, refining and reforming theutterance to develop a better understandable form (e.g., logical form)or structure for the utterance. Generating a response may include usingNLG techniques.

The NLU processing performed by a digital assistant, such as digitalassistant 106, can include various NLP related tasks such as sentenceparsing (e.g., tokenizing, lemmatizing, identifying part-of-speech tagsfor the sentence, identifying named entities in the sentence, generatingdependency trees to represent the sentence structure, splitting asentence into clauses, analyzing individual clauses, resolvinganaphoras, performing chunking, and the like). In certain examples, theNLU processing is performed by digital assistant 106 itself. In someother examples, digital assistant 106 may use other resources to performportions of the NLU processing. For example, the syntax and structure ofan input utterance sentence may be identified by processing the sentenceusing a parser, a part-of-speech tagger, and/or a NER. In oneimplementation, for the English language, a parser, a part-of-speechtagger, and a named entity recognizer such as ones provided by theStanford NLP Group are used for analyzing the sentence structure andsyntax. These are provided as part of the Stanford CoreNLP toolkit.

While the various examples provided in this disclosure show utterancesin the English language, this is meant only as an example. In certainexamples, digital assistant 106 is also capable of handling utterancesin languages other than English. Digital assistant 106 may providesubsystems (e.g., components implementing NLU functionality) that areconfigured for performing processing for different languages. Thesesubsystems may be implemented as pluggable units that can be calledusing service calls from an NLU core server. This makes the NLUprocessing flexible and extensible for each language, including allowingdifferent orders of processing. A language pack may be provided forindividual languages, where a language pack can register a list ofsubsystems that can be served from the NLU core server.

A digital assistant, such as digital assistant 106 depicted in FIG. 1,can be made available or accessible to its users 108 through a varietyof different channels, such as but not limited to, via certainapplications, via social media platforms, via various messaging servicesand applications, and other applications or channels. A single digitalassistant can have several channels configured for it so that it can berun on and be accessed by different services simultaneously.

A digital assistant or chatbot system generally contains or isassociated with one or more skills. In certain embodiments, these skillsare individual chatbots (referred to as skill bots) that are configuredto interact with users and fulfill specific types of tasks, such astracking inventory, submitting timecards, creating expense reports,ordering food, checking a bank account, making reservations, buying awidget, and the like. For example, for the embodiment depicted in FIG.1, digital assistant or chatbot system 106 includes skills 116-1, 116-2,116-3, and so on. For purposes of this disclosure, the terms “skill” and“skills” are used synonymously with the terms “skill bot” and “skillbots,” respectively.

Each skill associated with a digital assistant helps a user of thedigital assistant complete a task through a conversation with the user,where the conversation can include a combination of text or audio inputsprovided by the user and responses provided by the skill bots. Theseresponses may be in the form of text or audio messages to the userand/or using simple user interface elements (e.g., select lists) thatare presented to the user for the user to make selections.

There are various ways in which a skill or skill bot can be associatedor added to a digital assistant. In some instances, a skill bot can bedeveloped by an enterprise and then added to a digital assistant usingDABP 102. In other instances, a skill bot can be developed and createdusing DABP 102 and then added to a digital assistant created using DABP102. In yet other instances, DABP 102 provides an online digital store(referred to as a “skills store”) that offers multiple skills directedto a wide range of tasks. The skills offered through the skills storemay also expose various cloud services. In order to add a skill to adigital assistant being generated using DABP 102, a user of DABP 102 canaccess the skills store via DABP 102, select a desired skill, andindicate that the selected skill is to be added to the digital assistantcreated using DABP 102. A skill from the skills store can be added to adigital assistant as is or in a modified form (for example, a user ofDABP 102 may select and clone a particular skill bot provided by theskills store, make customizations or modifications to the selected skillbot, and then add the modified skill bot to a digital assistant createdusing DABP 102).

Various different architectures may be used to implement a digitalassistant or chatbot system. For example, in certain embodiments, thedigital assistants created and deployed using DABP 102 may beimplemented using a master bot/child (or sub) bot paradigm orarchitecture. According to this paradigm, a digital assistant isimplemented as a master bot that interacts with one or more child botsthat are skill bots. For example, in the embodiment depicted in FIG. 1,digital assistant 106 comprises a master bot 114 and skill bots 116-1,116-2, etc. that are child bots of master bot 114. In certain examples,digital assistant 106 is itself considered to act as the master bot.

A digital assistant implemented according to the master-child botarchitecture enables users of the digital assistant to interact withmultiple skills through a unified user interface, namely via the masterbot. When a user engages with a digital assistant, the user input isreceived by the master bot. The master bot then performs processing todetermine the meaning of the user input utterance. The master bot thendetermines whether the task requested by the user in the utterance canbe handled by the master bot itself, else the master bot selects anappropriate skill bot for handling the user request and routes theconversation to the selected skill bot. This enables a user to conversewith the digital assistant through a common single interface and stillprovide the capability to use several skill bots configured to performspecific tasks. For example, for a digital assistance developed for anenterprise, the master bot of the digital assistant may interface withskill bots with specific functionalities, such as a customerrelationship management (CRM) bot for performing functions related tocustomer relationship management, an enterprise resource planning (ERP)bot for performing functions related to enterprise resource planning, ahuman capital management (HCM) bot for performing functions related tohuman capital management, etc. This way the end user or consumer of thedigital assistant need only know how to access the digital assistantthrough the common master bot interface and behind the scenes multipleskill bots are provided for handling the user request.

In certain examples, in a master bot/child bots infrastructure, themaster bot is configured to be aware of the available list of skillbots. The master bot may have access to metadata that identifies thevarious available skill bots, and for each skill bot, the capabilitiesof the skill bot including the tasks that can be performed by the skillbot. Upon receiving a user request in the form of an utterance, themaster bot is configured to, from the multiple available skill bots,identify or predict a specific skill bot that can best serve or handlethe user request. The master bot then routes the utterance (or a portionof the utterance) to that specific skill bot for further handling.Control thus flows from the master bot to the skill bots. The master botcan support multiple input and output channels. In certain examples,routing may be performed with the aid of processing performed by one ormore available skill bots. For example, as discussed below, a skill botcan be trained to infer an intent for an utterance and to determinewhether the inferred intent matches an intent with which the skill botis configured. Thus, the routing performed by the master bot can involvethe skill bot communicating to the master bot an indication of whetherthe skill bot has been configured with an intent suitable for handlingthe utterance.

While the embodiment in FIG. 1 shows digital assistant 106 comprising amaster bot 114 and skill bots 116-1, 116-2, and 116-3, this is notintended to be limiting. A digital assistant can include various othercomponents (e.g., other systems and subsystems) that provide thefunctionalities of the digital assistant. These systems and subsystemsmay be implemented only in software (e.g., code, instructions stored ona computer-readable medium and executable by one or more processors), inhardware only, or in implementations that use a combination of softwareand hardware.

DABP 102 provides an infrastructure and various services and featuresthat enable a user of DABP 102 to create a digital assistant includingone or more skill bots associated with the digital assistant. In someinstances, a skill bot can be created by cloning an existing skill bot,for example, cloning a skill bot provided by the skills store. Aspreviously indicated, DABP 102 provides a skills store or skills catalogthat offers multiple skill bots for performing various tasks. A user ofDABP 102 can clone a skill bot from the skills store. As needed,modifications or customizations may be made to the cloned skill bot. Insome other instances, a user of DABP 102 created a skill bot fromscratch using tools and services offered by DABP 102. As previouslyindicated, the skills store or skills catalog provided by DABP 102 mayoffer multiple skill bots for performing various tasks.

In certain examples, at a high level, creating or customizing a skillbot involves the following steps:

(1) Configuring settings for a new skill bot

(2) Configuring one or more intents for the skill bot

(3) Configuring one or more entities for one or more intents

(4) Training the skill bot

(5) Creating a dialog flow for the skill bot

(6) Adding custom components to the skill bot as needed

(7) Testing and deploying the skill bot

Each of the above steps is briefly described below.

(1) Configuring settings for a new skill bot—Various settings may beconfigured for the skill bot. For example, a skill bot designer canspecify one or more invocation names for the skill bot being created.These invocation names can then be used by users of a digital assistantto explicitly invoke the skill bot. For example, a user can input aninvocation name in the user's utterance to explicitly invoke thecorresponding skill bot.

(2) Configuring one or more intents and associated example utterancesfor the skill bot—The skill bot designer specifies one or more intents(also referred to as bot intents) for a skill bot being created. Theskill bot is then trained based upon these specified intents. Theseintents represent categories or classes that the skill bot is trained toinfer for input utterances. Upon receiving an utterance, a trained skillbot infers an intent for the utterance, where the inferred intent isselected from the predefined set of intents used to train the skill bot.The skill bot then takes an appropriate action responsive to anutterance based upon the intent inferred for that utterance. In someinstances, the intents for a skill bot represent tasks that the skillbot can perform for users of the digital assistant. Each intent is givenan intent identifier or intent name. For example, for a skill bottrained for a bank, the intents specified for the skill bot may include“CheckBalance,” “TransferMoney,” “DepositCheck,” and the like.

For each intent defined for a skill bot, the skill bot designer may alsoprovide one or more example utterances that are representative of andillustrate the intent. These example utterances are meant to representutterances that a user may input to the skill bot for that intent. Forexample, for the CheckBalance intent, example utterances may include“What's my savings account balance?”, “How much is in my checkingaccount?”, “How much money do I have in my account,” and the like.Accordingly, various permutations of typical user utterances may bespecified as example utterances for an intent.

The intents and their associated example utterances are used as trainingdata to train the skill bot. Various different training techniques maybe used. As a result of this training, a predictive model is generatedthat is configured to take an utterance as input and output an intentinferred for the utterance by the predictive model. In some instances,input utterances are provided to an intent analysis engine, which isconfigured to use the trained model to predict or infer an intent forthe input utterance. The skill bot may then take one or more actionsbased upon the inferred intent.

(3) Configuring entities for one or more intents of the skill bot—Insome instances, additional context may be needed to enable the skill botto properly respond to a user utterance. For example, there may besituations where a user input utterance resolves to the same intent in askill bot. For instance, in the above example, utterances “What's mysavings account balance?” and “How much is in my checking account?” bothresolve to the same CheckBalance intent, but these utterances aredifferent requests asking for different things. To clarify suchrequests, one or more entities are added to an intent. Using the bankingskill bot example, an entity called AccountType, which defines valuescalled “checking” and “saving” may enable the skill bot to parse theuser request and respond appropriately. In the above example, while theutterances resolve to the same intent, the value associated with theAccountType entity is different for the two utterances. This enables theskill bot to perform possibly different actions for the two utterancesin spite of them resolving to the same intent. One or more entities canbe specified for certain intents configured for the skill bot. Entitiesare thus used to add context to the intent itself. Entities helpdescribe an intent more fully and enable the skill bot to complete auser request.

In certain examples, there are two types of entities: (a) built-inentities provided by DABP 102, and (2) custom entities that can bespecified by a skill bot designer. Built-in entities are genericentities that can be used with a wide variety of bots. Examples ofbuilt-in entities include, without limitation, entities related to time,date, addresses, numbers, email addresses, duration, recurring timeperiods, currencies, phone numbers, URLs, and the like. Custom entitiesare used for more customized applications. For example, for a bankingskill, an AccountType entity may be defined by the skill bot designerthat enables various banking transactions by checking the user input forkeywords like checking, savings, and credit cards, etc.

(4) Training the skill bot—A skill bot is configured to receive userinput in the form of utterances parse or otherwise process the receivedinput and identify or select an intent that is relevant to the receiveduser input. As indicated above, the skill bot has to be trained forthis. In certain embodiments, a skill bot is trained based upon theintents configured for the skill bot and the example utterancesassociated with the intents (collectively, the training data), so thatthe skill bot can resolve user input utterances to one of its configuredintents. In certain examples, the skill bot uses a predictive model thatis trained using the training data and allows the skill bot to discernwhat users say (or in some cases, are trying to say). DABP 102 providesvarious different training techniques that can be used by a skill botdesigner to train a skill bot, including various machine-learning basedtraining techniques, rules-based training techniques, and/orcombinations thereof. In certain examples, a portion (e.g., 80%) of thetraining data is used to train a skill bot model and another portion(e.g., the remaining 20%) is used to test or verify the model. Oncetrained, the trained model (also sometimes referred to as the trainedskill bot) can then be used to handle and respond to user utterances. Incertain cases, a user's utterance may be a question that requires only asingle answer and no further conversation. In order to handle suchsituations, a Q&A (question-and-answer) intent may be defined for askill bot. This enables a skill bot to output replies to user requestswithout having to update the dialog definition. Q&A intents are createdin a similar manner as regular intents. The dialog flow for Q&A intentscan be different from that for regular intents.

(5) Creating a dialog flow for the skill bot—A dialog flow specified fora skill bot describes how the skill bot reacts as different intents forthe skill bot are resolved responsive to received user input. The dialogflow defines operations or actions that a skill bot will take, e.g., howthe skill bot responds to user utterances, how the skill bot promptsusers for input, how the skill bot returns data. A dialog flow is like aflowchart that is followed by the skill bot. The skill bot designerspecifies a dialog flow using a language, such as markdown language. Incertain embodiments, a version of YAML called OBotML may be used tospecify a dialog flow for a skill bot. The dialog flow definition for askill bot acts as a model for the conversation itself, one that lets theskill bot designer choreograph the interactions between a skill bot andthe users that the skill bot services.

In certain examples, the dialog flow definition for a skill bot containsthree sections:

(a) a context section

(b) a default transitions section

(c) a states section

Context section—The skill bot designer can define variables that areused in a conversation flow in the context section. Other variables thatmay be named in the context section include, without limitation:variables for error handling, variables for built-in or custom entities,user variables that enable the skill bot to recognize and persist userpreferences, and the like.

Default transitions section—Transitions for a skill bot can be definedin the dialog flow states section or in the default transitions section.The transitions defined in the default transition section act as afallback and get triggered when there are no applicable transitionsdefined within a state, or the conditions required to trigger a statetransition cannot be met. The default transitions section can be used todefine routing that allows the skill bot to gracefully handle unexpecteduser actions.

States section—A dialog flow and its related operations are defined as asequence of transitory states, which manage the logic within the dialogflow. Each state node within a dialog flow definition names a componentthat provides the functionality needed at that point in the dialog.States are thus built around the components. A state containscomponent-specific properties and defines the transitions to otherstates that get triggered after the component executes.

Special case scenarios may be handled using the states sections. Forexample, there might be times when you want to provide users the optionto temporarily leave a first skill, they are engaged with to dosomething in a second skill within the digital assistant. For example,if a user is engaged in a conversation with a shopping skill (e.g., theuser has made some selections for purchase), the user may want to jumpto a banking skill (e.g., the user may want to ensure that he/she hasenough money for the purchase), and then return to the shopping skill tocomplete the user's order. To address this, an action in the first skillcan be configured to initiate an interaction with the second differentskill in the same digital assistant and then return to the originalflow.

(6) Adding custom components to the skill bot—As described above, statesspecified in a dialog flow for skill bot name components that providethe functionality needed corresponding to the states. Components enablea skill bot to perform functions. In certain embodiments, DABP 102provides a set of preconfigured components for performing a wide rangeof functions. A skill bot designer can select one of more of thesepreconfigured components and associate them with states in the dialogflow for a skill bot. The skill bot designer can also create custom ornew components using tools provided by DABP 102 and associate the customcomponents with one or more states in the dialog flow for a skill bot.

(7) Testing and deploying the skill bot—DABP 102 provides severalfeatures that enable the skill bot designer to test a skill bot beingdeveloped. The skill bot can then be deployed and included in a digitalassistant.

While the description above describes how to create a skill bot, similartechniques may also be used to create a digital assistant (or the masterbot). At the master bot or digital assistant level, built-in systemintents may be configured for the digital assistant. These built-insystem intents are used to identify general tasks that the digitalassistant itself (i.e., the master bot) can handle without invoking askill bot associated with the digital assistant. Examples of systemintents defined for a master bot include: (1) Exit: applies when theuser signals the desire to exit the current conversation or context inthe digital assistant; (2) Help: applies when the user asks for help ororientation; and (3) Unresolved Intent: applies to user input thatdoesn't match well with the exit and help intents. The digital assistantalso stores information about the one or more skill bots associated withthe digital assistant. This information enables the master bot to selecta particular skill bot for handling an utterance.

At the master bot or digital assistant level, when a user inputs aphrase or utterance to the digital assistant, the digital assistant isconfigured to perform processing to determine how to route the utteranceand the related conversation. The digital assistant determines thisusing a routing model, which can be rules-based, AI-based, or acombination thereof. The digital assistant uses the routing model todetermine whether the conversation corresponding to the user inpututterance is to be routed to a particular skill for handling, is to behandled by the digital assistant or master bot itself per a built-insystem intent or is to be handled as a different state in a currentconversation flow.

In certain embodiments, as part of this processing, the digitalassistant determines if the user input utterance explicitly identifies askill bot using its invocation name. If an invocation name is present inthe user input, then it is treated as explicit invocation of the skillbot corresponding to the invocation name. In such a scenario, thedigital assistant may route the user input to the explicitly invokedskill bot for further handling. If there is no specific or explicitinvocation, in certain embodiments, the digital assistant evaluates thereceived user input utterance and computes confidence scores for thesystem intents and the skill bots associated with the digital assistant.The score computed for a skill bot or system intent represents howlikely the user input is representative of a task that the skill bot isconfigured to perform or is representative of a system intent. Anysystem intent or skill bot with an associated computed confidence scoreexceeding a threshold value (e.g., a Confidence Threshold routingparameter) is selected as a candidate for further evaluation. Thedigital assistant then selects, from the identified candidates, aparticular system intent or a skill bot for further handling of the userinput utterance. In certain embodiments, after one or more skill botsare identified as candidates, the intents associated with thosecandidate skills are evaluated (according to the intent model for eachskill) and confidence scores are determined for each intent. In general,any intent that has a confidence score exceeding a threshold value(e.g., 70%) is treated as a candidate intent. If a particular skill botis selected, then the user utterance is routed to that skill bot forfurther processing. If a system intent is selected, then one or moreactions are performed by the master bot itself according to the selectedsystem intent.

FIG. 2 is a simplified block diagram of a master bot (MB) system 200according to certain embodiments. MB system 200 can be implemented insoftware only, hardware only, or a combination of hardware and software.MB system 200 includes a pre-processing subsystem 210, a multiple intentsubsystem (MIS) 220, an explicit invocation subsystem (EIS) 230, a skillbot invoker 240, and a data store 250. MB system 200 depicted in FIG. 2is merely an example of an arrangement of components in a master bot.One of ordinary skill in the art would recognize many possiblevariations, alternatives, and modifications. For example, in someimplementations, MB system 200 may have more or fewer systems orcomponents than those shown in FIG. 2, may combine two or moresubsystems, or may have a different configuration or arrangement ofsubsystems.

Pre-processing subsystem 210 receives an utterance “A” 202 from a userand processes the utterance through a language detector 212 and alanguage parser 214. As indicated above, an utterance can be provided invarious ways including audio or text. The utterance 202 can be asentence fragment, a complete sentence, multiple sentences, and thelike. Utterance 202 can include punctuation. For example, if theutterance 202 is provided as audio, the pre-processing subsystem 210 mayconvert the audio to text using a speech-to-text converter (not shown)that inserts punctuation marks into the resulting text, e.g., commas,semicolons, periods, etc.

Language detector 212 detects the language of the utterance 202 based onthe text of the utterance 202. The manner in which the utterance 202 ishandled depends on the language since each language has its own grammarand semantics. Differences between languages are taken intoconsideration when analyzing the syntax and structure of an utterance.

Language parser 214 parses the utterance 202 to extract part of speech(POS) tags for individual linguistic units (e.g., words) in theutterance 202. POS tags include, for example, noun (NN), pronoun (PN),verb (VB), and the like. Language parser 214 may also tokenize thelinguistic units of the utterance 202 (e.g., to convert each word into aseparate token) and lemmatize words. A lemma is the main form of a setof words as represented in a dictionary (e.g., “run” is the lemma forrun, runs, ran, running, etc.). Other types of pre-processing that thelanguage parser 214 can perform include chunking of compoundexpressions, e.g., combining “credit” and “card” into a singleexpression “credit card.” Language parser 214 may also identifyrelationships between the words in the utterance 202. For example, insome embodiments, the language parser 214 generates a dependency treethat indicates which part of the utterance (e.g., a particular noun) isa direct object, which part of the utterance is a preposition, and soon. The results of the processing performed by the language parser 214form extracted information 205 and are provided as input to MIS 220together with the utterance 202 itself.

As indicated above, the utterance 202 can include more than onesentence. For purposes of detecting multiple intents and explicitinvocation, the utterance 202 can be treated as a single unit even if itincludes multiple sentences. However, in certain embodiments,pre-processing can be performed, e.g., by the pre-processing subsystem210, to identify a single sentence among multiple sentences for multipleintents analysis and explicit invocation analysis. In general, theresults produced by MIS 220 and EIS 230 are substantially the sameregardless of whether the utterance 202 is processed at the level of anindividual sentence or as a single unit comprising multiple sentences.

MIS 220 determines whether the utterance 202 represents multipleintents. Although MIS 220 can detect the presence of multiple intents inthe utterance 202, the processing performed by MIS 220 does not involvedetermining whether the intents of the utterance 202 match to anyintents that have been configured for a bot. Instead, processing todetermine whether an intent of the utterance 202 matches a bot intentcan be performed by an intent classifier 242 of the MB system 200 or byan intent classifier of a skill bot (e.g., as shown in FIG. 3). Theprocessing performed by MIS 220 assumes that there exists a bot (e.g., aparticular skill bot or the master bot itself) that can handle theutterance 202. Therefore, the processing performed by MIS 220 does notrequire knowledge of what bots are in the chatbot system (e.g., theidentities of skill bots registered with the master bot) or knowledge ofwhat intents have been configured for a particular bot.

To determine that the utterance 202 includes multiple intents, the MIS220 applies one or more rules from a set of rules 252 in the data store250. The rules applied to the utterance 202 depend on the language ofthe utterance 202 and may include sentence patterns that indicate thepresence of multiple intents. For example, a sentence pattern mayinclude a coordinating conjunction that joins two parts (e.g.,conjuncts) of a sentence, where both parts correspond to a separateintent. If the utterance 202 matches the sentence pattern, it can beinferred that the utterance 202 represents multiple intents. It shouldbe noted that an utterance with multiple intents does not necessarilyhave different intents (e.g., intents directed to different bots or todifferent intents within the same bot). Instead, the utterance couldhave separate instances of the same intent (e.g. “Place a pizza orderusing payment account X, then place a pizza order using payment accountY”).

As part of determining that the utterance 202 represents multipleintents, the MIS 220 also determines what portions of the utterance 202are associated with each intent. MIS 220 constructs, for each intentrepresented in an utterance containing multiple intents, a new utterancefor separate processing in place of the original utterance, e.g., anutterance “B” 206 and an utterance “C” 208, as depicted in FIG. 2. Thus,the original utterance 202 can be split into two or more separateutterances that are handled one at a time. MIS 220 determines, using theextracted information 205 and/or from analysis of the utterance 202itself, which of the two or more utterances should be handled first. Forexample, MIS 220 may determine that the utterance 202 contains a markerword indicating that a particular intent should be handled first. Thenewly formed utterance corresponding to this particular intent (e.g.,one of utterance 206 or utterance 208) will be the first to be sent forfurther processing by EIS 230. After a conversation triggered by thefirst utterance has ended (or has been temporarily suspended), the nexthighest priority utterance (e.g., the other one of utterance 206 orutterance 208) can then be sent to the EIS 230 for processing.

EIS 230 determines whether the utterance that it receives (e.g.,utterance 206 or utterance 208) contains an invocation name of a skillbot. In certain embodiments, each skill bot in a chatbot system isassigned a unique invocation name that distinguishes the skill bot fromother skill bots in the chatbot system. A list of invocation names canbe maintained as part of skill bot information 254 in data store 250. Anutterance is deemed to be an explicit invocation when the utterancecontains a word match to an invocation name. If a bot is not explicitlyinvoked, then the utterance received by the EIS 230 is deemed anon-explicitly invoking utterance 234 and is input to an intentclassifier (e.g., intent classifier 242) of the master bot to determinewhich bot to use for handling the utterance. In some instances, theintent classifier 242 will determine that the master bot should handle anon-explicitly invoking utterance. In other instances, the intentclassifier 242 will determine a skill bot to route the utterance to forhandling.

The explicit invocation functionality provided by the EIS 230 hasseveral advantages. It can reduce the amount of processing that themaster bot has to perform. For example, when there is an explicitinvocation, the master bot may not have to do any intent classificationanalysis (e.g., using the intent classifier 242), or may have to doreduced intent classification analysis for selecting a skill bot. Thus,explicit invocation analysis may enable selection of a particular skillbot without resorting to intent classification analysis.

Also, there may be situations where there is an overlap infunctionalities between multiple skill bots. This may happen, forexample, if the intents handled by the two skill bots overlap or arevery close to each other. In such a situation, it may be difficult forthe master bot to identify which of the multiple skill bots to selectbased upon intent classification analysis alone. In such scenarios, theexplicit invocation disambiguates the particular skill bot to be used.

In addition to determining that an utterance is an explicit invocation,the EIS 230 is responsible for determining whether any portion of theutterance should be used as input to the skill bot being explicitlyinvoked. In particular, EIS 230 can determine whether part of theutterance is not associated with the invocation. The EIS 230 can performthis determination through analysis of the utterance and/or analysis ofthe extracted information 205. EIS 230 can send the part of theutterance not associated with the invocation to the invoked skill bot inlieu of sending the entire utterance that was received by the EIS 230.In some instances, the input to the invoked skill bot is formed simplyby removing any portion of the utterance associated with the invocation.For example, “I want to order pizza using Pizza Bot” can be shortened to“I want to order pizza” since “using Pizza Bot” is relevant to theinvocation of the pizza bot, but irrelevant to any processing to beperformed by the pizza bot. In some instances, EIS 230 may reformat thepart to be sent to the invoked bot, e.g., to form a complete sentence.Thus, the EIS 230 determines not only that there is an explicitinvocation, but also what to send to the skill bot when there is anexplicit invocation. In some instances, there may not be any text toinput to the bot being invoked. For example, if the utterance was “PizzaBot”, then the EIS 230 could determine that the pizza bot is beinginvoked, but there is no text to be processed by the pizza bot. In suchscenarios, the EIS 230 may indicate to the skill bot invoker 240 thatthere is nothing to send.

Skill bot invoker 240 invokes a skill bot in various ways. For instance,skill bot invoker 240 can invoke a bot in response to receiving anindication 235 that a particular skill bot has been selected as a resultof an explicit invocation. The indication 235 can be sent by the EIS 230together with the input for the explicitly invoked skill bot. In thisscenario, the skill bot invoker 240 will turn control of theconversation over to the explicitly invoked skill bot. The explicitlyinvoked skill bot will determine an appropriate response to the inputfrom the EIS 230 by treating the input as a stand-alone utterance. Forexample, the response could be to perform a specific action or to starta new conversation in a particular state, where the initial state of thenew conversation depends on the input sent from the EIS 230.

Another way in which skill bot invoker 240 can invoke a skill bot isthrough implicit invocation using the intent classifier 242. The intentclassifier 242 can be trained, using machine-learning and/or rules-basedtraining techniques, to determine a likelihood that an utterance isrepresentative of a task that a particular skill bot is configured toperform. The intent classifier 242 is trained on different classes, oneclass for each skill bot. For instance, whenever a new skill bot isregistered with the master bot, a list of example utterances associatedwith the new skill bot can be used to train the intent classifier 242 todetermine a likelihood that a particular utterance is representative ofa task that the new skill bot can perform. The parameters produced asresult of this training (e.g., a set of values for parameters of amachine-learning model) can be stored as part of skill bot information254.

In certain embodiments, the intent classifier 242 is implemented using amachine-learning model, as described in further detail herein. Trainingof the machine-learning model may involve inputting at least a subset ofutterances from the example utterances associated with various skillbots to generate, as an output of the machine-learning model, inferencesas to which bot is the correct bot for handling any particular trainingutterance. For each training utterance, an indication of the correct botto use for the training utterance may be provided as ground truthinformation. The behavior of the machine-learning model can then beadapted (e.g., through back-propagation) to minimize the differencebetween the generated inferences and the ground truth information.

In certain embodiments, the intent classifier 242 determines, for eachskill bot registered with the master bot, a confidence score indicatinga likelihood that the skill bot can handle an utterance (e.g., thenon-explicitly invoking utterance 234 received from EIS 230). The intentclassifier 242 may also determine a confidence score for each systemlevel intent (e.g., help, exit) that has been configured. If aparticular confidence score meets one or more conditions, then the skillbot invoker 240 will invoke the bot associated with the particularconfidence score. For example, a threshold confidence score value mayneed to be met. Thus, an output 245 of the intent classifier 242 iseither an identification of a system intent or an identification of aparticular skill bot. In some embodiments, in addition to meeting athreshold confidence score value, the confidence score must exceed thenext highest confidence score by a certain win margin. Imposing such acondition would enable routing to a particular skill bot when theconfidence scores of multiple skill bots each exceed the thresholdconfidence score value.

After identifying a bot based on evaluation of confidence scores, theskill bot invoker 240 hands over processing to the identified bot. Inthe case of a system intent, the identified bot is the master bot.Otherwise, the identified bot is a skill bot. Further, the skill botinvoker 240 will determine what to provide as input 247 for theidentified bot. As indicated above, in the case of an explicitinvocation, the input 247 can be based on a part of an utterance that isnot associated with the invocation, or the input 247 can be nothing(e.g., an empty string). In the case of an implicit invocation, theinput 247 can be the entire utterance.

Data store 250 comprises one or more computing devices that store dataused by the various subsystems of the master bot system 200. Asexplained above, the data store 250 includes rules 252 and skill botinformation 254. The rules 252 include, for example, rules fordetermining, by MIS 220, when an utterance represents multiple intentsand how to split an utterance that represents multiple intents. Therules 252 further include rules for determining, by EIS 230, which partsof an utterance that explicitly invokes a skill bot to send to the skillbot. The skill bot information 254 includes invocation names of skillbots in the chatbot system, e.g., a list of the invocation names of allskill bots registered with a particular master bot. The skill botinformation 254 can also include information used by intent classifier242 to determine a confidence score for each skill bot in the chatbotsystem, e.g., parameters of a machine-learning model.

FIG. 3 is a simplified block diagram of a skill bot system 300 accordingto certain embodiments. Skill bot system 300 is a computing system thatcan be implemented in software only, hardware only, or a combination ofhardware and software. In certain embodiments such as the embodimentdepicted in FIG. 1, skill bot system 300 can be used to implement one ormore skill bots within a digital assistant.

Skill bot system 300 includes an MIS 310, an intent classifier 320, anda conversation manager 330. The MIS 310 is analogous to the MIS 220 inFIG. 2 and provides similar functionality, including being operable todetermine, using rules 352 in a data store 350: (1) whether an utterancerepresents multiple intents and, if so, (2) how to split the utteranceinto a separate utterance for each intent of the multiple intents. Incertain embodiments, the rules applied by MIS 310 for detecting multipleintents and for splitting an utterance are the same as those applied byMIS 220. The MIS 310 receives an utterance 302 and extracted information304. The extracted information 304 is analogous to the extractedinformation 205 in FIG. 1 and can be generated using the language parser214 or a language parser local to the skill bot system 300.

Intent classifier 320 can be trained in a similar manner to the intentclassifier 242 discussed above in connection with the embodiment of FIG.2 and as described in further detail herein. For instance, in certainembodiments, the intent classifier 320 is implemented using amachine-learning model. The machine-learning model of the intentclassifier 320 is trained for a particular skill bot, using at least asubset of example utterances associated with that particular skill botas training utterances. The ground truth for each training utterancewould be the particular bot intent associated with the trainingutterance.

The utterance 302 can be received directly from the user or suppliedthrough a master bot. When the utterance 302 is supplied through amaster bot, e.g., as a result of processing through MIS 220 and EIS 230in the embodiment depicted in FIG. 2, the MIS 310 can be bypassed so asto avoid repeating processing already performed by MIS 220. However, ifthe utterance 302 is received directly from the user, e.g., during aconversation that occurs after routing to a skill bot, then MIS 310 canprocess the utterance 302 to determine whether the utterance 302represents multiple intents. If so, then MIS 310 applies one or morerules to split the utterance 302 into a separate utterance for eachintent, e.g., an utterance “D” 306 and an utterance “E” 308. Ifutterance 302 does not represent multiple intents, then MIS 310 forwardsthe utterance 302 to intent classifier 320 for intent classification andwithout splitting the utterance 302.

Intent classifier 320 is configured to match a received utterance (e.g.,utterance 306 or 308) to an intent associated with skill bot system 300.As explained above, a skill bot can be configured with one or moreintents, each intent including at least one example utterance that isassociated with the intent and used for training a classifier. In theembodiment of FIG. 2, the intent classifier 242 of the master bot system200 is trained to determine confidence scores for individual skill botsand confidence scores for system intents. Similarly, intent classifier320 can be trained to determine a confidence score for each intentassociated with the skill bot system 300. Whereas the classificationperformed by intent classifier 242 is at the bot level, theclassification performed by intent classifier 320 is at the intent leveland therefore finer grained. The intent classifier 320 has access tointents information 354. The intents information 354 includes, for eachintent associated with the skill bot system 300, a list of utterancesthat are representative of and illustrate the meaning of the intent andare typically associated with a task performable by that intent. Theintents information 354 can further include parameters produced as aresult of training on this list of utterances.

Conversation manager 330 receives, as an output of intent classifier320, an indication 322 of a particular intent, identified by the intentclassifier 320, as best matching the utterance that was input to theintent classifier 320. In some instances, the intent classifier 320 isunable to determine any match. For example, the confidence scorescomputed by the intent classifier 320 could fall below a thresholdconfidence score value if the utterance is directed to a system intentor an intent of a different skill bot. When this occurs, the skill botsystem 300 may refer the utterance to the master bot for handling, e.g.,to route to a different skill bot. However, if the intent classifier 320is successful in identifying an intent within the skill bot, then theconversation manager 330 will initiate a conversation with the user.

The conversation initiated by the conversation manager 330 is aconversation specific to the intent identified by the intent classifier320. For instance, the conversation manager 330 may be implemented usinga state machine configured to execute a dialog flow for the identifiedintent. The state machine can include a default starting state (e.g.,for when the intent is invoked without any additional input) and one ormore additional states, where each state has associated with it actionsto be performed by the skill bot (e.g., executing a purchasetransaction) and/or dialog (e.g., questions, responses) to be presentedto the user. Thus, the conversation manager 330 can determine anaction/dialog 335 upon receiving the indication 322 identifying theintent and can determine additional actions or dialog in response tosubsequent utterances received during the conversation.

Data store 350 comprises one or more computing devices that store dataused by the various subsystems of the skill bot system 300. As depictedin FIG. 3, the data store 350 includes the rules 352 and the intentsinformation 354. In certain embodiments, data store 350 can beintegrated into a data store of a master bot or digital assistant, e.g.,the data store 250 in FIG. 2.

Context Tag Integration

Intent prediction and entity extraction, which are the two majorcomponents of natural language processing, help chatbot systemsunderstand user queries and user utterances with respect to the domainof a given service or set of services. Intent prediction determines anobjective (i.e., intent) of the user's query or utterance. Entityextraction determines one or more constraints, if any, of the user'squery or utterance. For example, for a user inquiry regarding “theweather on Wednesday in the Poconos,” intent prediction determines thatthe user's intent is to learn about the “weather” and entity extractiondetermines that “Wednesday” and “Poconos” are constraints that focus theuser's intent to a particular day and geographical location. Entityextraction can involve matching, where the words of the user's query areconfirmed as entities by matching words to a list of pre-definedentities. However, because the subject of an entity may not alwayscorrespond to an obvious or popular referent, matching often fails toidentify the referent intended by the user. Matching is even morechallenging when a user's utterance is limited to one or two words perutterance or when a user's utterance contains more information than isnecessary. For example, in the case of a limited utterance, a userutterance involving just the word “2020” may refer to a specificcalendar year, specific cost of an item, quantity of an item, etc.,depending on the context in other user utterances or system queriesassociated with user's utterance. In the case of a user utterance withadditional information, a user utterance reciting “it is 2020 for 20people costing 2000” may or may not correspond to a year entity, a costentity, or quantity entity depending on the context of the user'sutterance. As can be seen from above examples, unless context isconsidered, the referent intended by the user in an utterance cannot beaccurately determined. Features of the present disclosure, as describedherein, overcome these challenges by evaluating the distribution ofcontext tags within a group of entities associated with a system queryor a user's utterance(s).

FIG. 4A shows a block diagram illustrating aspects of a chatbot system400 configured to train and utilize NER models within intent classifiers(e.g., the intent classifier 242 of FIG. 2 or intent classifier 320 ofFIG. 3). As shown in FIG. 4A, chatbot system 400 may include aprediction model training stage 410, a skill bot invocation stage 415configured to determine a likelihood that an utterance is representativeof a task that a particular skill bot is configured to perform, anintent prediction stage 420 configured to classify utterances as one ormore intents, and an entity detection stage 422 configured to determineone or more constraints 480 of the utterances. The prediction modeltraining stage 410 may be configured to build and train one or moreprediction models 425 a-425 n (which may be referred to hereinindividually as a prediction model or collectively as the predictionmodels) to be used by the other stages. In some examples, the predictionmodels can include a model for determining a likelihood that anutterance is representative of a task that a particular skill bot isconfigured to perform, a model for predicting an intent from anutterance for a first type of skill bot, a model for predicting anintent from an utterance for a second type of skill bot, and a model foridentifying mentions of conceptual entities in text and classifying themaccording to a given set of categories. Still other types of predictionmodels may be implemented in other examples according to thisdisclosure.

A prediction model can be a machine-learning (ML) model, such as aconvolutional neural network (CNN), e.g. an inception neural network, aresidual neural network (Resnet), or a recurrent neural network, e.g.,long short-term memory (LSTM) models, a bidirectional LSTM (BiLSTM) orgated recurrent units (GRU) models, other variants of deep neuralnetworks (DNN). A prediction model can also be any other suitable MLmodel trained for natural language processing, such as a bidirectionalencoder representations from transformers (BERT) model, naive bayesclassifier, linear classifier, support vector machine, conditionalrandom field model, random forest model, boosting models, shallow neuralnetworks, or combinations of one or more of such techniques (e.g.,CNN-HMM or MCNN). The chatbot system 400 may employ the same type ofprediction model or different types of prediction models for determininga likelihood of a task that a particular skill bot is configured toperform, predicting an intent from an utterance for a first type ofskill bot, predicting an intent from an utterance for a second type ofskill bot, and identifying mentions of conceptual entities in text andclassifying them according to a given set of categories. Still othertypes of prediction models may be implemented in other examplesaccording to this disclosure.

As further shown in FIG. 4A, prediction model training stage 410 mayinclude dataset preparation 430, feature engineering 435, and modeltraining 440. Dataset preparation 430 may be configured to process inputdata assets 445 into separate training and validation sets 445 a-n foreach prediction model. Data assets 445 may include at least a subset ofutterances from example utterances associated with various skill bots.As previously described, an utterance can be provided in various waysincluding audio or text. The utterance can be a sentence fragment, acomplete sentence, multiple sentences, and the like. If, for example,the utterance is provided as audio, data preparation 430 may convert theaudio to text using a speech-to-text converter (not shown) that insertspunctuation marks into the resulting text (e.g., commas, semicolons,periods, etc.). In some instances, the example utterances are providedby a client or customer. In other instances, the example utterances areautomatically generated from prior libraries of utterances (e.g.,identifying utterances from a library that are specific to a skill thata chatbot is to learn). In some examples, data assets 445 for aprediction model can include input text or audio (or input features oftext or audio frames) and corresponding labels 450 for the input text oraudio (or input features) as a matrix or table of values. For example,for each training utterance, an indication of the correct bot to use forthe training utterance may be provided as ground truth information forlabels 450. The behavior of a respective prediction model can then beadapted (e.g., through back-propagation) to minimize the differencebetween the generated inferences and the ground truth information.Alternatively, a prediction model may be trained for a particular skillbot, using at least a subset of example utterances associated with thatparticular skill bot as training utterances. The ground truthinformation for labels 450 for each training utterance would be theparticular bot intent associated with the training utterance.Alternatively, a prediction model may be trained for a particular skillbot, using at least a subset of example utterances associated with thatparticular skill bot as training utterances. The ground truthinformation for labels 450 for each training utterance would be theparticular bot intent associated with the training utterance.

In some instances, augmentation may be applied to the data assets 445.For example, Easy Data Augmentation (EDA) techniques may be used forboosting performance on text classification tasks. EDA includes fouroperations: synonym replacement, ransom insertion, random swap, andrandom deletion that prevent overfitting and helping train more robustmodels. Note that the EDA operations in general: (i) obtain words fromthe original text, and (ii) incorporate the words within each data asset445 relative to the original text. For example, synonym replacementoperation includes randomly selecting n words from the original sentence(e.g., utterance) that are not stop words, and replacing each of thesewords with one of its synonyms chosen at random. The random insertionoperation includes—n times—finding a random synonym of a random word inthe original sentence that is not a stop word and inserting that synonyminto a random position in the sentence. The random swap operationincludes—n times—randomly choosing two words in the sentence andswapping their positions. The random deletion operation includesrandomly removing each word in the sentence with probability p.

In some examples, feature engineering 435 may include transforming dataassets 445 into feature vectors and/or creating new features will becreated using the data assets 445. The feature vectors may include countvectors as features, TF-IDF vectors as features such as word level,n-gram level or character level, word embedding as features, text/NLP asfeatures, topic models as features, or a combination thereof. Countvector is a matrix notation of the data assets 445 in which every rowrepresents an utterance, every column represents a term from theutterance, and every cell represents the frequency count of a particularterm in a utterance. TF-IDF score represents the relative importance ofa term in the utterance. A word embedding is a form of representingwords and utterances using a dense vector representation. The positionof a word within the vector space is learned from text and is based onthe words that surround the word when it is used. Text/NLP basedfeatures may include word count in the utterance, character count in theutterance, average word density, punctuation count, upper case count,title word count, frequency distribution of part of speech tags (e.g.,nouns and verbs), or any combination thereof. Topic modelling is atechnique to identify the groups of words (called a topic) from acollection of utterances that contains best information in thecollection.

In some examples, model training 440 may include training a classifierusing the feature vectors and/or new features created in featureengineering 435. In some instances, the training process includesiterative operations to find a set of parameters for the predictionmodel that minimizes a loss or error function for the prediction models.Each iteration can involve finding a set of parameters for theprediction model so that the value of the loss or error function usingthe set of parameters is smaller than the value of the loss or errorfunction using another set of parameters in a previous iteration. Theloss or error function can be constructed to measure the differencebetween the outputs predicted using the prediction models and the labels450 contained in the data assets 445. Once the set of parameters areidentified, the prediction model has been trained and can be utilizedfor prediction as designed.

In addition to the data assets 445, labels 450, the feature vectorsand/or new features, other techniques and information can also beemployed to refine the training process of the prediction models. Forexample, the feature vectors and/or new features may be combinedtogether to help to improve the accuracy of the classifier or model.Additionally, or alternatively, the hyperparameters may be tuned oroptimized, for example, a number of parameters such as tree length,leaves, network parameters etc. can be fine-tuned to get a best fitmodel. Although the training mechanisms described herein mainly focus ontraining a prediction model. These training mechanisms can also beutilized to fine tune existing prediction models trained from other dataassets. For example, in some cases, a prediction model might have beenpre-trained using utterance specific to another skill bot. In thosecases, the prediction models can be retrained using the data assets 445as discussed herein.

In some examples, the prediction model training stage 410 may outputtrained prediction models including the task prediction models 460,intent prediction models 465, and entity extraction models 467. The taskprediction models 460 may be used in the skill bot invocation stage 415to determine a likelihood that an utterance is representative of a taskthat a particular skill bot is configured to perform 470, the intentprediction models 465 may be used in the intent prediction stage 420 forclassifying utterances as one or more intents 475, and the entityextraction models 467 may be used in the entity detection stage 422 forextracting and classifying entities as one or more constraints 480. Insome instances, the skill bot invocation stage 415, the intentprediction stage 420, and the entity detection stage 422 may proceedindependently in some examples with separate models. For example, thetrained intent prediction models 465 may be used in the intentprediction stage 420 to predict intents for skill bots and/or thetrained entity extraction models 467 may be used in the entity detectionstage 422 to extract and classify entities for skill bots without firstidentifying the skill bots in the skill bot invocation stage 415.Similarly, the task prediction models 460 may be used in the skill botinvocation stage 415 to predict tasks or skill bots to be used forutterances without identifying the intent and/or entities of theutterances in the intent prediction stage 420 and/or the entitydetection stage 422.

Alternatively, the skill bot invocation stage 415, the intent predictionstage 420, and the entity detection stage 422 may be conductedsequentially with one stage using the outputs of the other as inputs orone stage being invokes in a particular manner for a specific skill botbased on the outputs of the other. For instance, for a given text data405, a skill bot invoker can invoke a skill bot through implicitinvocation using the skill bot invocation stage 415 and the taskprediction models 460. The task prediction models 460 can be trained,using machine-learning and/or rules-based training techniques, todetermine a likelihood that an utterance is representative of a taskthat a particular skill bot 470 is configured to perform. Then for anidentified or invoked skill bot and a given text data 405, the intentprediction stage 420 and intent prediction models 465 and/or the entitydetection stage 422 and the entity extraction models 467 can be used tomatch a received utterance (e.g., utterance within given data asset 445)to an intent 475 associated with skill bot. As explained herein, a skillbot can be configured with one or more intents, each intent including atleast one example utterance that is associated with the intent and usedfor training a classifier. In some embodiments, the skill bot invocationstage 415, the task prediction models 460, and the entity extractionmodels 467 for the master bot system are trained to determine confidencescores for individual skill bots and confidence scores for systemintents. Similarly, the intent prediction stage 420 and intentprediction models 465 and/or the entity detection stage 422 and theentity extraction models 467 can be trained to determine a confidencescore for each intent associated with the skill bot system. Whereas theclassification performed by the skill bot invocation stage 415, the taskprediction models 460, and the entity extraction models 467 are at thebot level, the classification performed by the intent prediction stage420 and intent prediction models 465 and/or the entity detection stage422 and the entity extraction models 467 are at the intent level andtherefore finer grained.

In some examples, entities associated with entity extraction models 467are included in a group of entities defined by a gazetteer. For a bottrained with a particular skill, a gazetteer for that skill includes agroup of entities related to that skill. For example, for a bot trainedwith a banking skill, a gazetteer for that skill may include a group ofentities comprising a number entity (number tag), a date and time entity(date and time tag), a currency entity (currency tag), a person entity(person tag), and a location entity (location tag). In another example,for a bot trained with a food ordering skill, a gazetteer for that skillincludes a group of entities comprising a quantity entity (quantitytag), a type of food entity (food type tag), a time entity (time tag), aperson entity (person tag), and a location entity (location tag). Insome embodiments, entity detection stage 422 identifies a particularskill of the bot, extracts one or more groups of entities pertaining tothat particular skill from one or more gazetteers, detects entitieswithin the one or more groups of entities in one or more system queriesand/or one or more user utterances, labels the detected entities withone or more context tags, determines a confidence score for each contexttag (context tag distribution), and identifies one or more constraints480 based on the context tag distribution for the one or more groups ofentities.

In some examples, the context tag distribution is determined based onthe context of one or more system queries, one or more user utterances,and/or an entire interaction between the system and the user. In someexamples, a context tag for a detected entity may be given a highconfidence score relative to other entities within the group of entitiesor a lower confidence score relative to other entities within the groupof entities based on the context of one or more system queries, one ormore user utterances, and/or an entire interaction between the systemand the user. In some examples, if a bot queries a user about one ormore specific entities, one or more entities in the user's responsiveutterance(s) pertaining to the one or more specific entities queried bythe bot will be given a high confidence score relative to other entitiesdetected in the user's responsive utterance(s). For example, a botrelating to a particular skill (e.g., banking) may query a user about aspecific entity (e.g., a quantity) within a group of entitiescorresponding to that particular skill (e.g., quantity, date and time,location, person, and transaction type). A user's responsiveutterance(s) may include one or more detected entities including thespecific entity. In this case, the detected quantity entity (i.e.,quantity tag) in the user's responsive utterance(s) will be given a highconfidence score and the other detected entities (e.g., date and time,location, person, and transaction type) within the group of entities forthe particular skill will be given lower confidence scores. Similarly,if multiple occurrences of the same entity are detected in the bot'squery and/or user's utterance(s), then that entity will be given a highconfidence score relative to confidence scores given for the otherdetected entities (i.e., those occurring with less frequency).

On some occasions, all detected entities will be given the sameconfidence score. For example, if a bot does not query a user about aspecific entity and the user's responsive utterance(s) includesdifferent entities, each detected entity in the user's responsiveutterance(s) may be given the same confidence score. Similarly, if thebot's query and/or the user's responsive utterance(s) does not includemultiple occurrences of the same entity, each detected entity may begiven the same confidence score. In some cases, one or more detectedentities of the group of entities may be given a first confidence score,one or more detected entities of the group of entities may be given asecond confidence score, different from the first confidence score, andone or more detected entities of the group of entities may be given athird confidence score, different from the first and second confidencescores. The foregoing discussion is merely exemplary and not limited todetermining confidence scores based on specific entity inclusion andentity occurrence frequency. Other metrics for determining whichdetected entities within a group of entities are contextually relevant.For example, one or more detected entities of a group of entities may beconsidered by the bot to be more contextually relevant than one or moreother detected entities of the group of entities.

In some examples, the confidence scores for detected entities in thebot's query and/or user's utterance(s) form a context tag distribution.In some examples, the context tag distribution is a vector of confidencescores for all the detected entities within a group of entities for abot's query and/or user's utterance(s). Accordingly, by considering thecontext of one or more system queries, one or more user utterances,and/or an entire interaction between the system and the user, featuresof the present disclosure determine how contextually relevant one ormore detected entities within a group of entities may be to a systemquery and/or a user's utterance(s).

In some examples, entity detection stage 422 includes a baseline modelto predict one or more constraints 480 based on the context tagdistribution. In some examples, the baseline model may be pre-trained.Alternatively, in some examples, the baseline model may be initiallytrained and updated. In some examples, the baseline model may be trainedbased on a crowdsourced dataset. In some examples, the baseline modelmay be trained based on the Conference on Computational Natural LanguageLearning (CoNLL) dataset. In some examples, the baseline model 4000 isconfigured as shown in FIG. 4B. As shown in FIG. 4B, the baseline model4000 is configured with a transformer-based model, such as abidirectional encoder representations from transformers (BERT) model4400, a regular expression (RX)/gazetteer (GZ) vectorizer 4500, acontext tag vectorizer 4600, a sequence processing model, such as acombined convolutional neural network/bidirectional long short-termmemory (CNN/BiLSTM) model 4700, and a discriminative model, such asconditional random field (CRF) model 4800.

The BERT model 4400 is a pre-trained algorithm that accepts one or moresequences of words from a user utterance(s) or system query(ies) as aninput and generates one or more feature vectors (word embeddings) foreach of the one or more words of the one or more sequences of words. Forexample, as shown in FIG. 4B, for an input sequence of words “I wouldlike to pay Merchant $10,” BERT model 4400 generates a separate wordembedding for each individual word of the sequence (“I,” “would,”“like,” “to,” “pay,” “Merchant,” and “$10”). In some examples, BERTmodel 4400 includes at least one transformer layer for receiving theinput sequence of words. In some examples, the at least one transformerlayer includes a plurality of encoders. In some examples, each encoderincludes a plurality of attention mechanisms and a plurality offeed-forward networks. In some examples, the input sequence of words istokenized to generate a plurality of word tokens. In some examples, theplurality of attention mechanisms operates directly on the words of theinput of sequence of words. In some examples, the plurality of attentionmechanisms operates on the plurality of word tokens. In some examples,the plurality of attention mechanisms generates an attention score foreach word of the input sequence of words or each token of the pluralityof word tokens. In some examples, the input sequence of words (or theplurality of word tokens) and the attention scores are input into theplurality of feed-forward networks. In some examples, the plurality offeed-forward networks encodes the input sequence of words (or theplurality of word tokens) into a plurality of word embeddings.

The RX/GZ vectorizer 4500 generates one or more feature vectors based onone or more words of the input sequence of words that match(es) one ormore known regular expression patterns in one or more known gazetteers.For example, as shown in FIG. 4B, for the input sequence of words “Iwould like to pay Merchant $10,” RX/GZ vectorizer 4500 determines one ormore words of the input sequence of words (“Merchant,” “$10”) that matchone or more regular expression patterns (e.g., Merchants, ten dollars)of words listed in one or more gazetteers and extracts a pre-definedvector for each of the one or more matched words. In some examples, theone or more gazetteers is automatically selected based on the skill ofthe chatbot. In some examples, the one or more gazetteers is selected bya user of the chatbot. In some examples, the one or more gazetteers isselected by a user of the digital assistant that includes the chatbot.In some examples, a group of entities for each skill of a plurality ofchatbot skills is defined by one or more gazetteers. For example, for achatbot trained with a banking skill, a gazetteer for that chatbot skillincludes a group of entities including a number entity (number tag), adate and time entity (date and time tag), a currency entity (currencytag), a person entity (person tag), and a location entity (locationtag). In another example, for a chatbot trained with a pizza orderingskill, a gazetteer for that chatbot skill includes a group of entitiesincluding a quantity entity (quantity tag), a type entity (type tag), atoppings entity (toppings tag), an address entity (address tag), andcost entity (cost tag). In some examples, entities within a group ofentities defined by one or more gazetteers pertaining to a particularskill of the chatbot are matched to one or more words of the inputsequence of words. In some examples, a regular expression algorithmmatches regular patterns of one or more of the words of the inputsequence of words to regular expression patterns of the one or moreentities in the one or more groups of entities listed in the one or moregazetteers. In some examples, the one or more gazetteers include apre-defined vector for each entity and its associated regular expressionpatterns. In some examples, for every matched entity and matched regularexpression pattern, a pre-defined vector is extracted. For example, forthe input sequence of “I would like to pay Merchant $10,” RX/GZvectorizer 4500 extracts a pre-defined vector for the Merchant entityand a pre-defined vector for the $10 entity. In some examples, theplurality of word embeddings output from the BERT model 4400 areconcatenated and/or interpolated with one or more of the pre-definedvectors extracted from the RX/GZ vectorizer 4500 to generate aconcatenated and/or interpolated set of vectors.

The context tag vectorizer 4600 generates one or more vectors based onthe context tag distribution for the input sequence of words. Theprocess for determining a context tag distribution for one or moresystem queries and/or one or more user utterance(s) is described aboveand not repeated herein. However, to illustrate, as shown in FIG. 4B,for the input sequence of words “I would like to pay Merchant $10,” acontext tag distribution may be determined to be 0.5, 0.0, 0.0, 0.0,0.8, 0.8, 0.8 for detected entities within a group of entities listed ina gazetteer for a particular skill of the chatbot. In some examples, thecontext tag vectorizer 4600 transforms the context tag distribution intoone or more vectors. In some examples, one or more vectors generated bythe context tag vectorizer 4600 are concatenated and/or interpolatedwith the concatenated and/or interpolated set of vectors to generate afirst set of vector representations. In some embodiments, as describedbelow, one or more vectors generated by the context tag vectorizer 4600are concatenated and/or interpolated with one or more sentence-levelvector representations generated by the CNN/BiLSTM model 4700.

In some examples, the first set of vector representations is input intothe CNN/BiLSTM model 4700. Based on the first set of vectorrepresentations, the CNN of the CNN/BiLSTM model 4700 generates one ormore character-level vector representations for each character of eachword of the input sequence of words. The one or more character-levelvector representations is then concatenated and/or interpolated with thefirst set of vector representations and input into the BiLSTM network togenerate one or more sentence-level vector representations for the inputsequence of words. In some examples, the one or more sentence-levelvector representations represents named entity tag scores. In someexamples, one or more vectors generated by the context tag vectorizer4600 are concatenated and/or interpolated with one or moresentence-level vector representations generated by the CNN/BiLSTM model4700 to generate a second set of vector representations. In someexamples, the second set of vector representations represent namedentity tag scores. In some examples, the named entity tag scores aredecoded into named entities using CRF model 4800. For example, as shownin FIG. 4B, CRF model 4800 identifies “Business” and “Currency” as twonamed entities for the input sequence of words “I would like to payMerchant $10” based on the one or more sentence-level vectorrepresentations and/or the second set of vector representations for theinput sequence words.

FIG. 5 is a flowchart illustrating a process 500 for taking context intoconsideration for entity recognition (entity extraction andclassification) according to certain embodiments. The processingdepicted in FIG. 5 may be implemented in software (e.g., code,instructions, program) executed by one or more processing units (e.g.,processors, cores) of the respective systems, hardware, or combinationsthereof. The software may be stored on a non-transitory storage medium(e.g., on a memory device). The method presented in FIG. 5 and describedbelow is intended to be illustrative and non-limiting. Although FIG. 5depicts the various processing steps occurring in a particular sequenceor order, this is not intended to be limiting. In certain alternativeembodiments, the steps may be performed in some different order or somesteps may also be performed in parallel. In certain embodiments, such asin the embodiment depicted in FIGS. 1-4B, the processing depicted inFIG. 5 may be performed by a pre-processing subsystem (e.g.,pre-processing subsystem 210 or prediction model training stage 410) togenerate training sets with context labels for training by one or morepredictive models (e.g., entity extraction models 467).

At step 505, an utterance is received by chatbot system 400 (FIG. 4). Insome examples, the utterance may be received in response to a systemquery. In some examples, the utterance corresponds to one or more userutterance(s) in response to one or more system queries. In someexamples, the utterance corresponds to one or more user interactionswith the chatbot. In some examples, the utterance corresponds an inputsequence of words.

At step 510, embeddings for words of the utterance are generated. Insome examples, an embedding is generated for each word of the utterance.In some examples, embeddings are generated using a transformer-basedmodel, such as BERT model 4400 of FIG. 4B. Features and operations ofBERT model 4400 have been described above and are not repeated herein.

At step 515, RX/GZ feature vectors are generated and concatenated and/orinterpolated with the embeddings to generate a concatenated and/orinterpolated set of vectors. In some examples, RX/GZ feature vectors aregenerated using the RX/GZ vectorizer 4500 of FIG. 4B. In some examples,the RX/GZ vectorizer 4500 generates one or more feature vectors based onone or more words of the input sequence of words that match(es) one ormore known regular expression patterns in one or more known gazetteers.In some examples, the one or more gazetteers is automatically selectedbased on the skill of the chatbot. In some examples, the one or moregazetteers is selected by a user of the chatbot. In some examples, theone or more gazetteers is selected by a user of the digital assistantthat includes the chatbot. In some examples, a group of entities foreach skill of a plurality of chatbot skills is defined by one or moregazetteers. In some examples, entities within a group of entitiesdefined by one or more gazetteers pertaining to a particular skill ofthe chatbot are matched to one or more words of the input sequence ofwords. In some examples, a regular expression algorithm matches regularpatterns of one or more of the words of the input sequence of words toregular expression patterns of the one or more entities in the one ormore groups of entities listed in the one or more gazetteers. In someexamples, the one or more gazetteers include a pre-defined vector foreach entity and its associated regular expression patterns. In someexamples, for every matched entity and matched regular expressionpattern, a pre-defined vector is extracted. Other features andoperations of RX/GZ vectorizer 4500 have been described above and arenot repeated herein. In some examples, the plurality of word embeddingsoutput from the BERT model 4400 are concatenated and/or interpolatedwith the RXGZ feature vectors extracted from the RX/GZ vectorizer 4500to generate a concatenated and/or interpolated set of vectors.

At optional step 520, context tag distribution feature vectors aregenerated for the received utterance and concatenated and/orinterpolated with the concatenated and/or interpolated RX/GZ featurevectors and embeddings to form a first set of feature vectors. In someexamples, context tax distribution feature vectors are generated usingthe context tag vectorizer 4600 of FIG. 4B. In some examples, contexttag distribution is determined based on the context of one or moresystem queries, one or more user utterances, and/or an entireinteraction between the system and the user. In some examples, a contexttag for a detected entity may be given a high confidence score relativeto other entities within a group of entities or a lower confidence scorerelative to other entities within the group of entities based on thecontext of one or more system queries, one or more user utterances,and/or an entire interaction between the system and the user. In someexamples, if a bot queries a user about one or more specific entities,one or more entities in the user's responsive utterance(s) pertaining tothe one or more specific entities queried by the bot will be given ahigh confidence score relative to other entities detected in the user'sresponsive utterance(s). Similarly, if multiple occurrences of the sameentity are detected in the bot's query and/or user's utterance(s), thenthat entity will be given a high confidence score relative to confidencescores given for the other detected entities (i.e., those occurring withless frequency). On some occasions, all detected entities will be giventhe same confidence score. For example, if a bot does not query a userabout a specific entity and the user's responsive utterance(s) includesdifferent entities, each detected entity in the user's responsiveutterance(s) may be given the same confidence score. Similarly, if thebot's query and/or the user's responsive utterance(s) does not includemultiple occurrences of the same entity, each detected entity may begiven the same confidence score. In some cases, one or more detectedentities of the group of entities may be given a first confidence score,one or more detected entities of the group of entities may be given asecond confidence score, different from the first confidence score, andone or more detected entities of the group of entities may be given athird confidence score, different from the first and second confidencescores.

The foregoing discussion is merely exemplary and not limited todetermining confidence scores based on specific entity inclusion andentity occurrence frequency. Other metrics for determining whichdetected entities within a group of entities are contextually relevant.For example, one or more detected entities of a group of entities may beconsidered by the bot to be more contextually relevant than one or moreother detected entities of the group of entities. In some examples, theconfidence scores for detected entities in the bot's query and/or user'sutterance(s) form a context tag distribution. In some examples, thecontext tag distribution is a vector of confidence scores for all thedetected entities within a group of entities for a bot's query and/oruser's utterance(s). In some examples, the context tag vectorizer 4600generates one or more vectors based on the context tag distribution. Insome examples, the context tag vectorizer 4600 transforms the contexttag distribution into one or more context tags distribution vectors. Insome examples, the context tag distribution vectors generated by thecontext tag vectorizer 4600 are concatenated and/or interpolated withthe concatenated and/or interpolated set of vectors to generate thefirst set of feature vectors. In some embodiments, as described below,context tag distribution vectors generated by the context tag vectorizer4600 are concatenated and/or interpolated with the encoded form of theutterance generated by the CNN/BiLSTM model 4700.

At step 525, an encoded form of the utterance is generated based on theconcatenated and/or interpolated set of vectors. In some examples, ifthe optional step 520 is included, an encoded form of the utterance isgenerated based on the first set of feature vectors. The encoded form ofthe utterance is generated using a sequence processing model, such asthe CNN/BiLSTM model 4700 of FIG. 4B. In some examples, the concatenatedand/or interpolated set of vectors and/or the first set of featurevectors is input into the CNN/BiLSTM model 4700. Based on the inputtedvectors, the CNN of the CNN/BiLSTM model 4700 generates one or morecharacter-level vector representations for each character of each wordof the utterance. The one or more character-level vector representationsis then concatenated and/or interpolated with the concatenated and/orinterpolated set of vectors and/or the first set of feature vectors andinput into the BiLSTM network to generate one or more sentence-levelvector representations for utterance. In some examples, the encoded formof the utterance comprises the one or more sentence-level vectorrepresentations. In some examples, the encoded form of the utterancerepresents named entity tag scores.

At optional step 530, context tag distribution feature vectors aregenerated for the received utterance and concatenated and/orinterpolated with the encoded form of the utterance. In some examples,context tax distribution feature vectors are generated using the contexttag vectorizer 4600 of FIG. 4B. Features and operations of context tagvectorizer 4600 and context tag distribution feature vector generationhave been described above and are not repeated herein. In some examples,the encoded form of the utterance concatenated and/or interpolated withthe context tag distribution feature vectors represents name entity tagscore.

At step 535, log-probabilities for candidate entities are generatedbased on the encoded form of the utterance. Log-probabilities forcandidate entities may be generated using a discriminative model, suchas the CRF model 4800 of FIG. 4B. In some examples, thelog-probabilities are decoded by CRF model 4800 into named entities.

At step 540, the log-probabilities are used to identify one or moreconstraints 480 for the received utterance. In some examples, entitydetection stage 422 (FIG. 4A) uses the decoded named entities toidentify one or more constraints 480 for the received utterance.

At optional step 545, the one or more constraints 480 and one or moreintent predictions generated by the intent prediction stage 420 (FIG.4A) for the received utterance are matched to an intent 475 (FIG. 4A)associated with a skill bot.

Features of the present disclosure improve user interactions withdigital assistants and/or chatbot systems. For example, a user mayinteract with digital assistant/chatbot system 106, as shown in FIG. 1,to order a to conduct a banking transaction. A skill bot pertaining tothe banking transaction, such as Skill Bot #1 116-1, as shown in FIG. 1,may be invoked at Skill Bot Invocation stage 415. The interactionincludes one or more user utterances and one or system queries. For abanking transaction, the skill bot may inquire the user about specificbanking tasks, such as balance checks, deposit checks, transfers, etc.,the user wishes to be performed by the skill bot and/or digitalassistant. In response, the user may utter one or more utterancespertaining to the user's banking intent. In one example, the user mayutter “please deposit 20 in my account 20.” Based on the system,methods, and examples provided herein, as shown in FIGS. 1-5 anddescribed throughout, the digital assistant and/or chatbot system willbe able to correctly identify “deposit 20” as pertaining to the amountthe user wishes to deposit and “account 20” as pertaining to the accountthe user wishes to make the deposit along with the user's intent todeposit money into the user's account. As discussed throughout, byconsidering the context of one or more system queries, one or more userutterances, and/or an entire interaction between the system and theuser, features of the present disclosure determine how contextuallyrelevant one or more detected entities within a group of entities may beto a system query and/or a user's utterance(s), features of the presentdisclosure are able to correctly identify the intended referent for aparticular named entity and improve the user's interaction with thedigital assistant and/or chatbot system.

Illustrative Systems

FIG. 6 depicts a simplified diagram of a distributed system 600. In theillustrated example, distributed system 600 includes one or more clientcomputing devices 602, 604, 606, and 608, coupled to a server 612 viaone or more communication networks 610. Clients computing devices 602,604, 606, and 608 may be configured to execute one or more applications.

In various examples, server 612 may be adapted to run one or moreservices or software applications that enable one or more embodimentsdescribed in this disclosure. In certain examples, server 612 may alsoprovide other services or software applications that may includenon-virtual and virtual environments. In some examples, these servicesmay be offered as web-based or cloud services, such as under a Softwareas a Service (SaaS) model to the users of client computing devices 602,604, 606, and/or 608. Users operating client computing devices 602, 604,606, and/or 608 may in turn utilize one or more client applications tointeract with server 612 to utilize the services provided by thesecomponents.

In the configuration depicted in FIG. 6, server 612 may include one ormore components 618, 620 and 622 that implement the functions performedby server 612. These components may include software components that maybe executed by one or more processors, hardware components, orcombinations thereof. It should be appreciated that various differentsystem configurations are possible, which may be different fromdistributed system 600. The example shown in FIG. 6 is thus one exampleof a distributed system for implementing an example system and is notintended to be limiting.

Users may use client computing devices 602, 604, 606, and/or 608 toexecute one or more applications, models or chatbots, which may generateone or more events or models that may then be implemented or serviced inaccordance with the teachings of this disclosure. A client device mayprovide an interface that enables a user of the client device tointeract with the client device. The client device may also outputinformation to the user via this interface. Although FIG. 6 depicts onlyfour client computing devices, any number of client computing devicesmay be supported.

The client devices may include various types of computing systems suchas portable handheld devices, general purpose computers such as personalcomputers and laptops, workstation computers, wearable devices, gamingsystems, thin clients, various messaging devices, sensors or othersensing devices, and the like. These computing devices may run varioustypes and versions of software applications and operating systems (e.g.,Microsoft Windows®, Apple Macintosh®, UNIX® or UNIX-like operatingsystems, Linux or Linux-like operating systems such as Google Chrome™OS) including various mobile operating systems (e.g., Microsoft WindowsMobile®, iOS®, Windows Phone®, Android™, BlackBerry®, Palm OS®).Portable handheld devices may include cellular phones, smartphones,(e.g., an iPhone), tablets (e.g., iPad®), personal digital assistants(PDAs), and the like. Wearable devices may include Google Glass® headmounted display, and other devices. Gaming systems may include varioushandheld gaming devices, Internet-enabled gaming devices (e.g., aMicrosoft Xbox® gaming console with or without a Kinect® gesture inputdevice, Sony PlayStation® system, various gaming systems provided byNintendo®, and others), and the like. The client devices may be capableof executing various different applications such as variousInternet-related apps, communication applications (e.g., E-mailapplications, short message service (SMS) applications) and may usevarious communication protocols.

Network(s) 610 may be any type of network familiar to those skilled inthe art that may support data communications using any of a variety ofavailable protocols, including without limitation TCP/IP (transmissioncontrol protocol/Internet protocol), SNA (systems network architecture),IPX (Internet packet exchange), AppleTalk®, and the like. Merely by wayof example, network(s) 610 may be a local area network (LAN), networksbased on Ethernet, Token-Ring, a wide-area network (WAN), the Internet,a virtual network, a virtual private network (VPN), an intranet, anextranet, a public switched telephone network (PSTN), an infra-rednetwork, a wireless network (e.g., a network operating under any of theInstitute of Electrical and Electronics (IEEE) 1002.11 suite ofprotocols, Bluetooth®, and/or any other wireless protocol), and/or anycombination of these and/or other networks.

Server 612 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. Server 612 mayinclude one or more virtual machines running virtual operating systems,or other computing architectures involving virtualization such as one ormore flexible pools of logical storage devices that may be virtualizedto maintain virtual storage devices for the server. In various examples,server 612 may be adapted to run one or more services or softwareapplications that provide the functionality described in the foregoingdisclosure.

The computing systems in server 612 may run one or more operatingsystems including any of those discussed above, as well as anycommercially available server operating system. Server 612 may also runany of a variety of additional server applications and/or mid-tierapplications, including HTTP (hypertext transport protocol) servers, FTP(file transfer protocol) servers, CGI (common gateway interface)servers, JAVA® servers, database servers, and the like. Exemplarydatabase servers include without limitation those commercially availablefrom Oracle®, Microsoft®, Sybase®, IBM® (International BusinessMachines), and the like.

In some implementations, server 612 may include one or more applicationsto analyze and consolidate data feeds and/or event updates received fromusers of client computing devices 602, 604, 606, and 608. As an example,data feeds and/or event updates may include, but are not limited to,Twitter® feeds, Facebook® updates or real-time updates received from oneor more third party information sources and continuous data streams,which may include real-time events related to sensor data applications,financial tickers, network performance measuring tools (e.g., networkmonitoring and traffic management applications), clickstream analysistools, automobile traffic monitoring, and the like. Server 612 may alsoinclude one or more applications to display the data feeds and/orreal-time events via one or more display devices of client computingdevices 602, 604, 606, and 608.

Distributed system 600 may also include one or more data repositories614, 616. These data repositories may be used to store data and otherinformation in certain examples. For example, one or more of the datarepositories 614, 616 may be used to store information such asinformation related to chatbot performance or generated models for useby chatbots used by server 612 when performing various functions inaccordance with various embodiments. Data repositories 614, 616 mayreside in a variety of locations. For example, a data repository used byserver 612 may be local to server 612 or may be remote from server 612and in communication with server 612 via a network-based or dedicatedconnection. Data repositories 614, 616 may be of different types. Incertain examples, a data repository used by server 612 may be adatabase, for example, a relational database, such as databases providedby Oracle Corporation® and other vendors. One or more of these databasesmay be adapted to enable storage, update, and retrieval of data to andfrom the database in response to SQL-formatted commands.

In certain examples, one or more of data repositories 614, 616 may alsobe used by applications to store application data. The data repositoriesused by applications may be of different types such as, for example, akey-value store repository, an object store repository, or a generalstorage repository supported by a file system.

In certain examples, the functionalities described in this disclosuremay be offered as services via a cloud environment. FIG. 7 is asimplified block diagram of a cloud-based system environment in whichvarious services may be offered as cloud services in accordance withcertain examples. In the example depicted in FIG. 7, cloudinfrastructure system 702 may provide one or more cloud services thatmay be requested by users using one or more client computing devices704, 706, and 708. Cloud infrastructure system 702 may comprise one ormore computers and/or servers that may include those described above forserver 612. The computers in cloud infrastructure system 702 may beorganized as general-purpose computers, specialized server computers,server farms, server clusters, or any other appropriate arrangementand/or combination.

Network(s) 710 may facilitate communication and exchange of data betweenclients 704, 706, and 708 and cloud infrastructure system 702.Network(s) 710 may include one or more networks. The networks may be ofthe same or different types. Network(s) 710 may support one or morecommunication protocols, including wired and/or wireless protocols, forfacilitating the communications.

The example depicted in FIG. 7 is only one example of a cloudinfrastructure system and is not intended to be limiting. It should beappreciated that, in some other examples, cloud infrastructure system702 may have more or fewer components than those depicted in FIG. 7, maycombine two or more components, or may have a different configuration orarrangement of components. For example, although FIG. 7 depicts threeclient computing devices, any number of client computing devices may besupported in alternative examples.

The term cloud service is generally used to refer to a service that ismade available to users on demand and via a communication network suchas the Internet by systems (e.g., cloud infrastructure system 702) of aservice provider. Typically, in a public cloud environment, servers andsystems that make up the cloud service provider's system are differentfrom the customer's own on-premise servers and systems. The cloudservice provider's systems are managed by the cloud service provider.Customers may thus avail themselves of cloud services provided by acloud service provider without having to purchase separate licenses,support, or hardware and software resources for the services. Forexample, a cloud service provider's system may host an application, anda user may, via the Internet, on demand, order and use the applicationwithout the user having to buy infrastructure resources for executingthe application. Cloud services are designed to provide easy, scalableaccess to applications, resources and services. Several providers offercloud services. For example, several cloud services are offered byOracle Corporation® of Redwood Shores, Calif., such as middlewareservices, database services, Java cloud services, and others.

In certain examples, cloud infrastructure system 702 may provide one ormore cloud services using different models such as under a Software as aService (SaaS) model, a Platform as a Service (PaaS) model, anInfrastructure as a Service (IaaS) model, and others, including hybridservice models. Cloud infrastructure system 702 may include a suite ofapplications, middleware, databases, and other resources that enableprovision of the various cloud services.

A SaaS model enables an application or software to be delivered to acustomer over a communication network like the Internet, as a service,without the customer having to buy the hardware or software for theunderlying application. For example, a SaaS model may be used to providecustomers access to on-demand applications that are hosted by cloudinfrastructure system 702. Examples of SaaS services provided by OracleCorporation® include, without limitation, various services for humanresources/capital management, customer relationship management (CRM),enterprise resource planning (ERP), supply chain management (SCM),enterprise performance management (EPM), analytics services, socialapplications, and others.

An IaaS model is generally used to provide infrastructure resources(e.g., servers, storage, hardware and networking resources) to acustomer as a cloud service to provide elastic compute and storagecapabilities. Various IaaS services are provided by Oracle Corporation®.

A PaaS model is generally used to provide, as a service, platform andenvironment resources that enable customers to develop, run, and manageapplications and services without the customer having to procure, build,or maintain such resources. Examples of PaaS services provided by OracleCorporation® include, without limitation, Oracle Java Cloud Service(JCS), Oracle Database Cloud Service (DBCS), data management cloudservice, various application development solutions services, and others.

Cloud services are generally provided on an on-demand self-servicebasis, subscription-based, elastically scalable, reliable, highlyavailable, and secure manner. For example, a customer, via asubscription order, may order one or more services provided by cloudinfrastructure system 702. Cloud infrastructure system 702 then performsprocessing to provide the services requested in the customer'ssubscription order. For example, a user may use utterances to requestthe cloud infrastructure system to take a certain action (e.g., anintent), as described above, and/or provide services for a chatbotsystem as described herein. Cloud infrastructure system 702 may beconfigured to provide one or even multiple cloud services.

Cloud infrastructure system 702 may provide the cloud services viadifferent deployment models. In a public cloud model, cloudinfrastructure system 702 may be owned by a third party cloud servicesprovider and the cloud services are offered to any general publiccustomer, where the customer may be an individual or an enterprise. Incertain other examples, under a private cloud model, cloudinfrastructure system 702 may be operated within an organization (e.g.,within an enterprise organization) and services provided to customersthat are within the organization. For example, the customers may bevarious departments of an enterprise such as the Human Resourcesdepartment, the Payroll department, etc. or even individuals within theenterprise. In certain other examples, under a community cloud model,the cloud infrastructure system 702 and the services provided may beshared by several organizations in a related community. Various othermodels such as hybrids of the above mentioned models may also be used.

Client computing devices 704, 706, and 708 may be of different types(such as client computing devices 602, 604, 606, and 608 depicted inFIG. 6) and may be capable of operating one or more client applications.A user may use a client device to interact with cloud infrastructuresystem 702, such as to request a service provided by cloudinfrastructure system 702. For example, a user may use a client deviceto request information or action from a chatbot as described in thisdisclosure.

In some examples, the processing performed by cloud infrastructuresystem 702 for providing services may involve model training anddeployment. This analysis may involve using, analyzing, and manipulatingdata sets to train and deploy one or more models. This analysis may beperformed by one or more processors, possibly processing the data inparallel, performing simulations using the data, and the like. Forexample, big data analysis may be performed by cloud infrastructuresystem 702 for generating and training one or more models for a chatbotsystem. The data used for this analysis may include structured data(e.g., data stored in a database or structured according to a structuredmodel) and/or unstructured data (e.g., data blobs (binary largeobjects)).

As depicted in the example in FIG. 7, cloud infrastructure system 702may include infrastructure resources 730 that are utilized forfacilitating the provision of various cloud services offered by cloudinfrastructure system 702. Infrastructure resources 730 may include, forexample, processing resources, storage or memory resources, networkingresources, and the like. In certain examples, the storage virtualmachines that are available for servicing storage requested fromapplications may be part of cloud infrastructure system 702. In otherexamples, the storage virtual machines may be part of different systems.

In certain examples, to facilitate efficient provisioning of theseresources for supporting the various cloud services provided by cloudinfrastructure system 702 for different customers, the resources may bebundled into sets of resources or resource modules (also referred to as“pods”). Each resource module or pod may comprise a pre-integrated andoptimized combination of resources of one or more types. In certainexamples, different pods may be pre-provisioned for different types ofcloud services. For example, a first set of pods may be provisioned fora database service, a second set of pods, which may include a differentcombination of resources than a pod in the first set of pods, may beprovisioned for Java service, and the like. For some services, theresources allocated for provisioning the services may be shared betweenthe services.

Cloud infrastructure system 702 may itself internally use services 732that are shared by different components of cloud infrastructure system702 and which facilitate the provisioning of services by cloudinfrastructure system 702. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and whitelist service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

Cloud infrastructure system 702 may comprise multiple subsystems. Thesesubsystems may be implemented in software, or hardware, or combinationsthereof. As depicted in FIG. 7, the subsystems may include a userinterface subsystem 712 that enables users or customers of cloudinfrastructure system 702 to interact with cloud infrastructure system702. User interface subsystem 712 may include various differentinterfaces such as a web interface 714, an online store interface 716where cloud services provided by cloud infrastructure system 702 areadvertised and are purchasable by a consumer, and other interfaces 718.For example, a customer may, using a client device, request (servicerequest 734) one or more services provided by cloud infrastructuresystem 702 using one or more of interfaces 714, 716, and 718. Forexample, a customer may access the online store, browse cloud servicesoffered by cloud infrastructure system 702, and place a subscriptionorder for one or more services offered by cloud infrastructure system702 that the customer wishes to subscribe to. The service request mayinclude information identifying the customer and one or more servicesthat the customer desires to subscribe to. For example, a customer mayplace a subscription order for a service offered by cloud infrastructuresystem 702. As part of the order, the customer may provide informationidentifying a chatbot system for which the service is to be provided andoptionally one or more credentials for the chatbot system.

In certain examples, such as the example depicted in FIG. 7, cloudinfrastructure system 702 may comprise an order management subsystem(OMS) 720 that is configured to process the new order. As part of thisprocessing, OMS 720 may be configured to: create an account for thecustomer, if not done already; receive billing and/or accountinginformation from the customer that is to be used for billing thecustomer for providing the requested service to the customer; verify thecustomer information; upon verification, book the order for thecustomer; and orchestrate various workflows to prepare the order forprovisioning.

Once properly validated, OMS 720 may then invoke the order provisioningsubsystem (OPS) 724 that is configured to provision resources for theorder including processing, memory, and networking resources. Theprovisioning may include allocating resources for the order andconfiguring the resources to facilitate the service requested by thecustomer order. The manner in which resources are provisioned for anorder and the type of the provisioned resources may depend upon the typeof cloud service that has been ordered by the customer. For example,according to one workflow, OPS 724 may be configured to determine theparticular cloud service being requested and identify a number of podsthat may have been pre-configured for that particular cloud service. Thenumber of pods that are allocated for an order may depend upon thesize/amount/level/scope of the requested service. For example, thenumber of pods to be allocated may be determined based upon the numberof users to be supported by the service, the duration of time for whichthe service is being requested, and the like. The allocated pods maythen be customized for the particular requesting customer for providingthe requested service.

In certain examples, setup phase processing, as described above, may beperformed by cloud infrastructure system 702 as part of the provisioningprocess. Cloud infrastructure system 702 may generate an application IDand select a storage virtual machine for an application from amongstorage virtual machines provided by cloud infrastructure system 702itself or from storage virtual machines provided by other systems otherthan cloud infrastructure system 702.

Cloud infrastructure system 702 may send a response or notification 744to the requesting customer to indicate when the requested service is nowready for use. In some instances, information (e.g., a link) may be sentto the customer that enables the customer to start using and availingthe benefits of the requested services. In certain examples, for acustomer requesting the service, the response may include a chatbotsystem ID generated by cloud infrastructure system 702 and informationidentifying a chatbot system selected by cloud infrastructure system 702for the chatbot system corresponding to the chatbot system ID.

Cloud infrastructure system 702 may provide services to multiplecustomers. For each customer, cloud infrastructure system 702 isresponsible for managing information related to one or more subscriptionorders received from the customer, maintaining customer data related tothe orders, and providing the requested services to the customer. Cloudinfrastructure system 702 may also collect usage statistics regarding acustomer's use of subscribed services. For example, statistics may becollected for the amount of storage used, the amount of datatransferred, the number of users, and the amount of system up time andsystem down time, and the like. This usage information may be used tobill the customer. Billing may be done, for example, on a monthly cycle.

Cloud infrastructure system 702 may provide services to multiplecustomers in parallel. Cloud infrastructure system 702 may storeinformation for these customers, including possibly proprietaryinformation. In certain examples, cloud infrastructure system 702comprises an identity management subsystem (IMS) 728 that is configuredto manage customer information and provide the separation of the managedinformation such that information related to one customer is notaccessible by another customer. IMS 728 may be configured to providevarious security-related services such as identity services, such asinformation access management, authentication and authorizationservices, services for managing customer identities and roles andrelated capabilities, and the like.

FIG. 8 illustrates an example of computer system 800. In some examples,computer system 800 may be used to implement any of the digitalassistant or chatbot systems within a distributed environment, andvarious servers and computer systems described above. As shown in FIG.8, computer system 800 includes various subsystems including aprocessing subsystem 804 that communicates with a number of othersubsystems via a bus subsystem 802. These other subsystems may include aprocessing acceleration unit 806, an I/O subsystem 808, a storagesubsystem 818, and a communications subsystem 824. Storage subsystem 818may include non-transitory computer-readable storage media includingstorage media 822 and a system memory 810.

Bus subsystem 802 provides a mechanism for letting the variouscomponents and subsystems of computer system 800 communicate with eachother as intended. Although bus subsystem 802 is shown schematically asa single bus, alternative examples of the bus subsystem may utilizemultiple buses. Bus subsystem 802 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, a local bus using any of a variety of bus architectures, and thelike. For example, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which may beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard, and the like.

Processing subsystem 804 controls the operation of computer system 800and may comprise one or more processors, application specific integratedcircuits (ASICs), or field programmable gate arrays (FPGAs). Theprocessors may include be single core or multicore processors. Theprocessing resources of computer system 800 may be organized into one ormore processing units 832, 834, etc. A processing unit may include oneor more processors, one or more cores from the same or differentprocessors, a combination of cores and processors, or other combinationsof cores and processors. In some examples, processing subsystem 804 mayinclude one or more special purpose co-processors such as graphicsprocessors, digital signal processors (DSPs), or the like. In someexamples, some or all of the processing units of processing subsystem804 may be implemented using customized circuits, such as applicationspecific integrated circuits (ASICs), or field programmable gate arrays(FPGAs).

In some examples, the processing units in processing subsystem 804 mayexecute instructions stored in system memory 810 or on computer readablestorage media 822. In various examples, the processing units may executea variety of programs or code instructions and may maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed may be resident in system memory810 and/or on computer-readable storage media 822 including potentiallyon one or more storage devices. Through suitable programming, processingsubsystem 804 may provide various functionalities described above. Ininstances where computer system 800 is executing one or more virtualmachines, one or more processing units may be allocated to each virtualmachine.

In certain examples, a processing acceleration unit 806 may optionallybe provided for performing customized processing or for off-loading someof the processing performed by processing subsystem 804 so as toaccelerate the overall processing performed by computer system 800.

I/O subsystem 808 may include devices and mechanisms for inputtinginformation to computer system 800 and/or for outputting informationfrom or via computer system 800. In general, use of the term inputdevice is intended to include all possible types of devices andmechanisms for inputting information to computer system 800. Userinterface input devices may include, for example, a keyboard, pointingdevices such as a mouse or trackball, a touchpad or touch screenincorporated into a display, a scroll wheel, a click wheel, a dial, abutton, a switch, a keypad, audio input devices with voice commandrecognition systems, microphones, and other types of input devices. Userinterface input devices may also include motion sensing and/or gesturerecognition devices such as the Microsoft Kinect® motion sensor thatenables users to control and interact with an input device, theMicrosoft Xbox® 360 game controller, devices that provide an interfacefor receiving input using gestures and spoken commands. User interfaceinput devices may also include eye gesture recognition devices such asthe Google Glass® blink detector that detects eye activity (e.g.,“blinking” while taking pictures and/or making a menu selection) fromusers and transforms the eye gestures as inputs to an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator) through voicecommands.

Other examples of user interface input devices include, withoutlimitation, three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader 3D scanners, 3Dprinters, laser rangefinders, and eye gaze tracking devices.Additionally, user interface input devices may include, for example,medical imaging input devices such as computed tomography, magneticresonance imaging, position emission tomography, and medicalultrasonography devices. User interface input devices may also include,for example, audio input devices such as MIDI keyboards, digital musicalinstruments and the like.

In general, use of the term output device is intended to include allpossible types of devices and mechanisms for outputting information fromcomputer system 800 to a user or other computer. User interface outputdevices may include a display subsystem, indicator lights, or non-visualdisplays such as audio output devices, etc. The display subsystem may bea cathode ray tube (CRT), a flat-panel device, such as that using aliquid crystal display (LCD) or plasma display, a projection device, atouch screen, and the like. For example, user interface output devicesmay include, without limitation, a variety of display devices thatvisually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Storage subsystem 818 provides a repository or data store for storinginformation and data that is used by computer system 800. Storagesubsystem 818 provides a tangible non-transitory computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some examples. Storage subsystem 818may store software (e.g., programs, code modules, instructions) thatwhen executed by processing subsystem 804 provides the functionalitydescribed above. The software may be executed by one or more processingunits of processing subsystem 804. Storage subsystem 818 may alsoprovide authentication in accordance with the teachings of thisdisclosure.

Storage subsystem 818 may include one or more non-transitory memorydevices, including volatile and non-volatile memory devices. As shown inFIG. 8, storage subsystem 818 includes a system memory 810 and acomputer-readable storage media 822. System memory 810 may include anumber of memories including a volatile main random access memory (RAM)for storage of instructions and data during program execution and anon-volatile read only memory (ROM) or flash memory in which fixedinstructions are stored. In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 800, such as duringstart-up, may typically be stored in the ROM. The RAM typically containsdata and/or program modules that are presently being operated andexecuted by processing subsystem 804. In some implementations, systemmemory 810 may include multiple different types of memory, such asstatic random access memory (SRAM), dynamic random access memory (DRAM),and the like.

By way of example, and not limitation, as depicted in FIG. 8, systemmemory 810 may load application programs 812 that are being executed,which may include various applications such as Web browsers, mid-tierapplications, relational database management systems (RDBMS), etc.,program data 814, and an operating system 816. By way of example,operating system 816 may include various versions of Microsoft Windows®,Apple Macintosh®, and/or Linux operating systems, a variety ofcommercially-available UNIX® or UNIX-like operating systems (includingwithout limitation the variety of GNU/Linux operating systems, theGoogle Chrome® OS, and the like) and/or mobile operating systems such asiOS, Windows® Phone, Android® OS, BlackBerry® OS, Palm® OS operatingsystems, and others.

Computer-readable storage media 822 may store programming and dataconstructs that provide the functionality of some examples.Computer-readable media 822 may provide storage of computer-readableinstructions, data structures, program modules, and other data forcomputer system 800. Software (programs, code modules, instructions)that, when executed by processing subsystem 804 provides thefunctionality described above, may be stored in storage subsystem 818.By way of example, computer-readable storage media 822 may includenon-volatile memory such as a hard disk drive, a magnetic disk drive, anoptical disk drive such as a CD ROM, DVD, a Blu-Ray® disk, or otheroptical media. Computer-readable storage media 822 may include, but isnot limited to, Zip® drives, flash memory cards, universal serial bus(USB) flash drives, secure digital (SD) cards, DVD disks, digital videotape, and the like. Computer-readable storage media 822 may alsoinclude, solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magneto resistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.

In certain examples, storage subsystem 818 may also include acomputer-readable storage media reader 820 that may further be connectedto computer-readable storage media 822. Reader 820 may receive and beconfigured to read data from a memory device such as a disk, a flashdrive, etc.

In certain examples, computer system 800 may support virtualizationtechnologies, including but not limited to virtualization of processingand memory resources. For example, computer system 800 may providesupport for executing one or more virtual machines. In certain examples,computer system 800 may execute a program such as a hypervisor thatfacilitated the configuring and managing of the virtual machines. Eachvirtual machine may be allocated memory, compute (e.g., processors,cores), I/O, and networking resources. Each virtual machine generallyruns independently of the other virtual machines. A virtual machinetypically runs its own operating system, which may be the same as ordifferent from the operating systems executed by other virtual machinesexecuted by computer system 800. Accordingly, multiple operating systemsmay potentially be run concurrently by computer system 800.

Communications subsystem 824 provides an interface to other computersystems and networks. Communications subsystem 824 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 800. For example, communications subsystem 824 mayenable computer system 800 to establish a communication channel to oneor more client devices via the Internet for receiving and sendinginformation from and to the client devices. For example, when computersystem 800 is used to implement bot system 120 depicted in FIG. 1, thecommunication subsystem may be used to communicate with a chatbot systemselected for an application.

Communication subsystem 824 may support both wired and/or wirelesscommunication protocols. In certain examples, communications subsystem824 may include radio frequency (RF) transceiver components foraccessing wireless voice and/or data networks (e.g., using cellulartelephone technology, advanced data network technology, such as 3G, 4Gor EDGE (enhanced data rates for global evolution), Wi-Fi (IEEE 802.XXfamily standards, or other mobile communication technologies, or anycombination thereof), global positioning system (GPS) receivercomponents, and/or other components. In some examples, communicationssubsystem 824 may provide wired network connectivity (e.g., Ethernet) inaddition to or instead of a wireless interface.

Communication subsystem 824 may receive and transmit data in variousforms. In some examples, in addition to other forms, communicationssubsystem 824 may receive input communications in the form of structuredand/or unstructured data feeds 826, event streams 828, event updates830, and the like. For example, communications subsystem 824 may beconfigured to receive (or send) data feeds 826 in real-time from usersof social media networks and/or other communication services such asTwitter® feeds, Facebook® updates, web feeds such as Rich Site Summary(RSS) feeds, and/or real-time updates from one or more third partyinformation sources.

In certain examples, communications subsystem 824 may be configured toreceive data in the form of continuous data streams, which may includeevent streams 828 of real-time events and/or event updates 830, that maybe continuous or unbounded in nature with no explicit end. Examples ofapplications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g., network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 824 may also be configured to communicate datafrom computer system 800 to other computer systems or networks. The datamay be communicated in various different forms such as structured and/orunstructured data feeds 826, event streams 828, event updates 830, andthe like to one or more databases that may be in communication with oneor more streaming data source computers coupled to computer system 800.

Computer system 800 may be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a personal computer, a workstation, a mainframe, a kiosk, aserver rack, or any other data processing system. Due to theever-changing nature of computers and networks, the description ofcomputer system 800 depicted in FIG. 8 is intended only as a specificexample. Many other configurations having more or fewer components thanthe system depicted in FIG. 8 are possible. Based on the disclosure andteachings provided herein, it should be appreciated there are other waysand/or methods to implement the various examples.

Although specific examples have been described, various modifications,alterations, alternative constructions, and equivalents are possible.Examples are not restricted to operation within certain specific dataprocessing environments but are free to operate within a plurality ofdata processing environments. Additionally, although certain exampleshave been described using a particular series of transactions and steps,it should be apparent to those skilled in the art that this is notintended to be limiting. Although some flowcharts describe operations asa sequential process, many of the operations may be performed inparallel or concurrently. In addition, the order of the operations maybe rearranged. A process may have additional steps not included in thefigure. Various features and aspects of the above-described examples maybe used individually or jointly.

Further, while certain examples have been described using a particularcombination of hardware and software, it should be recognized that othercombinations of hardware and software are also possible. Certainexamples may be implemented only in hardware, or only in software, orusing combinations thereof. The various processes described herein maybe implemented on the same processor or different processors in anycombination.

Where devices, systems, components or modules are described as beingconfigured to perform certain operations or functions, suchconfiguration may be accomplished, for example, by designing electroniccircuits to perform the operation, by programming programmableelectronic circuits (such as microprocessors) to perform the operationsuch as by executing computer instructions or code, or processors orcores programmed to execute code or instructions stored on anon-transitory memory medium, or any combination thereof. Processes maycommunicate using a variety of techniques including but not limited toconventional techniques for inter-process communications, and differentpairs of processes may use different techniques, or the same pair ofprocesses may use different techniques at different times.

Specific details are given in this disclosure to provide a thoroughunderstanding of the examples. However, examples may be practicedwithout these specific details. For example, well-known circuits,processes, algorithms, structures, and techniques have been shownwithout unnecessary detail in order to avoid obscuring the examples.This description provides example examples only, and is not intended tolimit the scope, applicability, or configuration of other examples.Rather, the preceding description of the examples will provide thoseskilled in the art with an enabling description for implementing variousexamples. Various changes may be made in the function and arrangement ofelements.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that additions, subtractions, deletions, and other modificationsand changes may be made thereunto without departing from the broaderspirit and scope as set forth in the claims. Thus, although specificexamples have been described, these are not intended to be limiting.Various modifications and equivalents are within the scope of thefollowing claims.

In the foregoing specification, aspects of the disclosure are describedwith reference to specific examples thereof, but those skilled in theart will recognize that the disclosure is not limited thereto. Variousfeatures and aspects of the above-described disclosure may be usedindividually or jointly. Further, examples may be utilized in any numberof environments and applications beyond those described herein withoutdeparting from the broader spirit and scope of the specification. Thespecification and drawings are, accordingly, to be regarded asillustrative rather than restrictive.

In the foregoing description, for the purposes of illustration, methodswere described in a particular order. It should be appreciated that inalternate examples, the methods may be performed in a different orderthan that described. It should also be appreciated that the methodsdescribed above may be performed by hardware components or may beembodied in sequences of machine-executable instructions, which may beused to cause a machine, such as a general-purpose or special-purposeprocessor or logic circuits programmed with the instructions to performthe methods. These machine-executable instructions may be stored on oneor more machine readable mediums, such as CD-ROMs or other type ofoptical disks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magneticor optical cards, flash memory, or other types of machine-readablemediums suitable for storing electronic instructions. Alternatively, themethods may be performed by a combination of hardware and software.

Where components are described as being configured to perform certainoperations, such configuration may be accomplished, for example, bydesigning electronic circuits or other hardware to perform theoperation, by programming programmable electronic circuits (e.g.,microprocessors, or other suitable electronic circuits) to perform theoperation, or any combination thereof.

While illustrative examples of the application have been described indetail herein, it is to be understood that the inventive concepts may beotherwise variously embodied and employed, and that the appended claimsare intended to be construed to include such variations, except aslimited by the prior art.

What is claimed is:
 1. A method, comprising: receiving, at a chatbotsystem comprising a processor, at least one utterance comprising one ormore words; generating, by a transformer-based model of the chatbotsystem, a plurality of embeddings for the one or more words of the atleast one utterance; generating, by a first vectorizer of the chatbotsystem, at least one regular expression and gazetteer feature vector forthe at least one utterance; generating, by a second vectorizer of thechatbot system, at least one context tag distribution feature vector forthe at least one utterance; concatenating or interpolating the pluralityof embeddings with the at least one regular expression and gazetteerfeature vector and the at least one context tag distribution featurevector to generate a first set of feature vectors; generating, by a mainsequence model of the chatbot system, an encoded form of the at leastone utterance based on the first set of feature vectors; generating, bya discriminative model of the chatbot system, a plurality oflog-probabilities for candidate entities based on the encoded form ofthe at least one utterance; and identifying, using the plurality oflog-probabilities, one or more constraints for the at least oneutterance based on the candidate entities.
 2. The method of claim 1,wherein the least one utterance comprises at least one of one or morequeries of the chatbot system, one or more queries input to the chatbotsystem by a user, one or more responses provided by the user in responseto the one or more queries of the chatbot system, or combinationthereof.
 3. The method of claim 1, wherein the transformer-based modelof the chatbot system comprises of a bidirectional encoderrepresentations from transformers model.
 4. The method of claim 1,wherein the first vectorizer generates the at least one regularexpression and gazetteer feature vector based on one or more regularexpression patterns and one or more gazetteers.
 5. The method of claim1, wherein the second vectorizer generates the at least one context tagdistribution feature vector based on a context of at least one of one ormore queries of the chatbot system, one or more queries input to thechatbot system by a user, one or more responses provided by the user inresponse to the one or more queries of the chatbot system, or acombination thereof.
 6. The method of claim 1, wherein the main sequencemodel of the chatbot system comprises a combined convolutional neuralnetwork/bidirectional long short-term memory model.
 7. The method ofclaim 1, wherein the discriminative model of the chatbot systemcomprises a conditional random field model.
 8. A chatbot systemcomprising: one or more processors; and a memory coupled to the one ormore processors, the memory storing a plurality of instructionsexecutable by the one or more processors, the plurality of instructionscomprising instructions that when executed by the one or more processorscause the one or more processors to: receive, at the chatbot system, atleast one utterance comprising one or more words; generate, with atransformer-based model, a plurality of embeddings for the one or morewords of the at least one utterance; generate, with a first vectorizer,at least one regular expression and gazetteer feature vector for the atleast one utterance; generate, with a second vectorizer, at least onecontext tag distribution feature vector for the at least one utterance;concatenate or interpolate the plurality of embeddings with the at leastone regular expression and gazetteer feature vector and the at least onecontext tag distribution feature vector to generate a first set offeature vectors; generate, with a main sequence model, an encoded formof the at least one utterance based on the first set of feature vectors;generate, with a discriminative model, a plurality of log-probabilitiesfor candidate entities based on the encoded form of the at least oneutterance; and identify, using the plurality of log-probabilities, oneor more constraints for the at least one utterance based on thecandidate entities.
 9. The chatbot system of claim 8, wherein the leastone utterance comprises at least one of one or more queries of thechatbot system, one or more queries input to the chatbot system by auser, one or more responses provided by the user in response to the oneor more queries of the chatbot system, or combination thereof.
 10. Thechatbot system of claim 8, wherein the transformer-based model comprisesa bidirectional encoder representations from transformers model.
 11. Thechatbot system of claim 8, wherein the first vectorizer generates the atleast one regular expression and gazetteer feature vector based on oneor more regular expression patterns and one or more gazetteers.
 12. Thechatbot system of claim 8, wherein the second vectorizer generates theat least one context tag distribution feature vector based on a contextof at least one of one or more queries of the chatbot system, one ormore queries input to the chatbot system by a user, one or moreresponses provided by the user in response to the one or more queries ofthe chatbot system, or a combination thereof.
 13. The chatbot system ofclaim 8, wherein the main sequence model comprises a combinedconvolutional neural network/bidirectional long short-term memory model.14. The chatbot system of claim 8, wherein the discriminative modelcomprises a conditional random field model.
 15. A non-transitorycomputer-readable memory storing a plurality of instructions executableby one or more processors, the plurality of instructions comprisinginstructions that when executed by the one or more processors cause theone or more processors to: receive, at a chatbot system, at least oneutterance comprising one or more words; generate, with atransformer-based model of the chatbot system, a plurality of embeddingsfor the one or more words of the at least one utterance; generate, witha first vectorizer of the chatbot system, at least one regularexpression and gazetteer feature vector for the at least one utterance;generate, with a second vectorizer of the chatbot system, at least onecontext tag distribution feature vector for the at least one utterance;concatenate or interpolate the plurality of embeddings with the at leastone regular expression and gazetteer feature vector and the at least onecontext tag distribution feature vector to generate a first set offeature vectors; generate, with a main sequence model of the chatbotsystem, an encoded form of the at least one utterance based on the firstset of feature vectors; generate, with a discriminative model of thechatbot system, a plurality of log-probabilities for candidate entitiesbased on the encoded form of the at least one utterance; and identify,using the plurality of log-probabilities, one or more constraints forthe at least one utterance based on the candidate entities.
 16. Thenon-transitory computer-readable memory of claim 15, wherein thetransformer-based model of the chatbot system comprises a bidirectionalencoder representations from transformers model.
 17. The non-transitorycomputer-readable memory of claim 15, wherein the first vectorizer ofthe chatbot system generates the at least one regular expression andgazetteer feature vector based on one or more regular expressionpatterns and one or more gazetteers.
 18. The non-transitorycomputer-readable memory of claim 15, wherein the second vectorizer ofthe chatbot system generates the at least one context tag distributionfeature vector based on a context of at least one of one or more queriesof the chatbot system, one or more queries input to the chatbot systemby a user, one or more responses provided by the user in response to theone or more queries of the chatbot system, or a combination thereof. 19.The non-transitory computer-readable memory of claim 15, wherein themain sequence model of the chatbot system comprises a combinedconvolutional neural network/bidirectional long short-term memory model.20. The non-transitory computer-readable memory of claim 15, wherein thediscriminative model of the chatbot system comprises a conditionalrandom field model.